diff --git a/notebooks/compare-dicom-with-jpg.ipynb b/notebooks/compare-dicom-with-jpg.ipynb
new file mode 100644
index 0000000..f96648e
--- /dev/null
+++ b/notebooks/compare-dicom-with-jpg.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import os\n",
+    "from collections import OrderedDict\n",
+    "from pathlib import Path\n",
+    "import uuid\n",
+    "import pydicom\n",
+    "\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import gzip\n",
+    "\n",
+    "# mimic-cxr should have two subfolders:\n",
+    "#   files/ - with MIMIC-CXR DICOMs\n",
+    "#   jpg/files/ - with MIMIC-CXR-JPG JPG files\n",
+    "mimic_cxr_path = Path('/db/mimic-cxr')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the record list\n",
+    "df = pd.read_csv(mimic_cxr_path / 'cxr-record-list.csv.gz', header=0, sep=',')\n",
+    "\n",
+    "\n",
+    "\n",
+    "fn = df.iloc[0]['path']\n",
+    "dicom_path = mimic_cxr_path / fn\n",
+    "\n",
+    "with open(dicom_path, 'rb') as fp:\n",
+    "    plan = pydicom.read_file(fp, stop_before_pixels=False)\n",
+    "\n",
+    "# load in JPG\n",
+    "jpg_path = (mimic_cxr_path / 'jpg') / (fn[:-4] + '.jpg')\n",
+    "img = cv2.imread(str(jpg_path), 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== files/p10/p10000032/s50414267/02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.dcm ===\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# annotate the figure with subfigure caption\n",
+    "text_kwargs = {'color': 'w', 'fontsize': 32, 'fontweight': 4, 'ha': 'center'}\n",
+    "yloc = 2900\n",
+    "\n",
+    "print(f'=== {fn} ===')\n",
+    "ax = plt.figure(figsize=[16, 10]).subplots(1, 2)\n",
+    "\n",
+    "# DICOM image\n",
+    "ax[0].imshow(plan.pixel_array, cmap='gray')\n",
+    "\n",
+    "xloc = np.mean(ax[0].get_xlim())\n",
+    "ax[0].text(xloc, yloc, '(a)', **text_kwargs)\n",
+    "ax[0].axis('off')\n",
+    "\n",
+    "# JPG image\n",
+    "ax[1].imshow(img, cmap='gray')\n",
+    "\n",
+    "xloc = np.mean(ax[1].get_xlim())\n",
+    "ax[1].text(xloc, yloc, '(b)', **text_kwargs)\n",
+    "ax[1].axis('off')\n",
+    "plt.savefig('dicom2jpg.jpg', bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mimic-cxr",
+   "language": "python",
+   "name": "mimic-cxr"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/generate-stats-from-paper.ipynb b/notebooks/generate-stats-from-paper.ipynb
index 54e2f7d..bbda561 100644
--- a/notebooks/generate-stats-from-paper.ipynb
+++ b/notebooks/generate-stats-from-paper.ipynb
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -69,90 +69,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dicom_id</th>\n",
-       "      <th>study_id</th>\n",
-       "      <th>subject_id</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>02aa804e-bde0afdd-112c0b34-7bc16630-4e384014</td>\n",
-       "      <td>50414267</td>\n",
-       "      <td>10000032</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>174413ec-4ec4c1f7-34ea26b7-c5f994f8-79ef1962</td>\n",
-       "      <td>50414267</td>\n",
-       "      <td>10000032</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab</td>\n",
-       "      <td>53189527</td>\n",
-       "      <td>10000032</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>e084de3b-be89b11e-20fe3f9f-9c8d8dfe-4cfd202c</td>\n",
-       "      <td>53189527</td>\n",
-       "      <td>10000032</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714</td>\n",
-       "      <td>53911762</td>\n",
-       "      <td>10000032</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       dicom_id  study_id  subject_id  split\n",
-       "0  02aa804e-bde0afdd-112c0b34-7bc16630-4e384014  50414267    10000032  train\n",
-       "1  174413ec-4ec4c1f7-34ea26b7-c5f994f8-79ef1962  50414267    10000032  train\n",
-       "2  2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab  53189527    10000032  train\n",
-       "3  e084de3b-be89b11e-20fe3f9f-9c8d8dfe-4cfd202c  53189527    10000032  train\n",
-       "4  68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714  53911762    10000032  train"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_split = pd.read_csv(mimic_cxr_path / 'mimic-cxr-2.0.0-split.csv.gz')\n",
     "df_metadata = pd.read_csv(mimic_cxr_path / 'mimic-cxr-2.0.0-metadata.csv.gz')"
@@ -167,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +454,7 @@
        "Support Devices                                                      NaN  "
       ]
      },
-     "execution_count": 49,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -559,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -590,77 +509,70 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>Number of images</th>\n",
+       "      <td>368960</td>\n",
+       "      <td>2991</td>\n",
+       "      <td>5159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>frontal</th>\n",
-       "      <td>248020.000000</td>\n",
-       "      <td>2041.00000</td>\n",
-       "      <td>3653.000000</td>\n",
+       "      <td>248020 (67.2%)</td>\n",
+       "      <td>2041 (68.2%)</td>\n",
+       "      <td>3653 (70.8%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lateral</th>\n",
-       "      <td>120795.000000</td>\n",
-       "      <td>949.00000</td>\n",
-       "      <td>1502.000000</td>\n",
+       "      <td>120795 (32.7%)</td>\n",
+       "      <td>949 (31.7%)</td>\n",
+       "      <td>1502 (29.1%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>other</th>\n",
-       "      <td>145.000000</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>4.000000</td>\n",
+       "      <td>145 (0.0%)</td>\n",
+       "      <td>1 (0.0%)</td>\n",
+       "      <td>4 (0.1%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Number of studies</th>\n",
-       "      <td>222758.000000</td>\n",
-       "      <td>1808.00000</td>\n",
-       "      <td>3269.000000</td>\n",
+       "      <td>222758</td>\n",
+       "      <td>1808</td>\n",
+       "      <td>3269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>with a finding</th>\n",
-       "      <td>170420.000000</td>\n",
-       "      <td>1394.00000</td>\n",
-       "      <td>2912.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>with a finding (%)</th>\n",
-       "      <td>76.504548</td>\n",
-       "      <td>77.10177</td>\n",
-       "      <td>89.079229</td>\n",
+       "      <td>170420 (76.5%)</td>\n",
+       "      <td>1394 (77.1%)</td>\n",
+       "      <td>2912 (89.1%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Number of patients</th>\n",
-       "      <td>64586.000000</td>\n",
-       "      <td>500.00000</td>\n",
-       "      <td>293.000000</td>\n",
+       "      <td>64586</td>\n",
+       "      <td>500</td>\n",
+       "      <td>293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>with a finding</th>\n",
-       "      <td>44157.000000</td>\n",
-       "      <td>344.00000</td>\n",
-       "      <td>288.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>with a finding (%)</th>\n",
-       "      <td>68.369306</td>\n",
-       "      <td>68.80000</td>\n",
-       "      <td>98.293515</td>\n",
+       "      <td>44157 (68.4%)</td>\n",
+       "      <td>344 (68.8%)</td>\n",
+       "      <td>288 (98.3%)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                              train    validate         test\n",
-       "frontal               248020.000000  2041.00000  3653.000000\n",
-       "lateral               120795.000000   949.00000  1502.000000\n",
-       "other                    145.000000     1.00000     4.000000\n",
-       "Number of studies     222758.000000  1808.00000  3269.000000\n",
-       "  with a finding      170420.000000  1394.00000  2912.000000\n",
-       "  with a finding (%)      76.504548    77.10177    89.079229\n",
-       "Number of patients     64586.000000   500.00000   293.000000\n",
-       "  with a finding       44157.000000   344.00000   288.000000\n",
-       "  with a finding (%)      68.369306    68.80000    98.293515"
+       "                             train      validate          test\n",
+       "Number of images            368960          2991          5159\n",
+       "frontal             248020 (67.2%)  2041 (68.2%)  3653 (70.8%)\n",
+       "lateral             120795 (32.7%)   949 (31.7%)  1502 (29.1%)\n",
+       "other                   145 (0.0%)      1 (0.0%)      4 (0.1%)\n",
+       "Number of studies           222758          1808          3269\n",
+       "  with a finding    170420 (76.5%)  1394 (77.1%)  2912 (89.1%)\n",
+       "Number of patients           64586           500           293\n",
+       "  with a finding     44157 (68.4%)   344 (68.8%)   288 (98.3%)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -670,14 +582,33 @@
     "split_views = df.groupby(['split', 'view'])[['dicom_id']].count()\n",
     "\n",
     "row_idx = ['frontal', 'lateral', 'other']\n",
-    "tbl = None\n",
+    "\n",
+    "# number of images in each set\n",
+    "n_images = {}\n",
+    "for c in splits:\n",
+    "    n_images[c] = split_views.loc[c].loc[row_idx, 'dicom_id'].sum()\n",
+    "\n",
+    "tbl = pd.DataFrame.from_dict(n_images, orient='index')\n",
+    "tbl.columns = ['Number of images']\n",
+    "tbl = tbl.T\n",
+    "\n",
+    "# number of images in each set for each view\n",
+    "n_images = {}\n",
     "for c in splits:\n",
-    "    if tbl is None:\n",
-    "        tbl = split_views.loc[c].loc[row_idx, 'dicom_id']\n",
-    "    else:\n",
-    "        tbl = pd.concat([tbl, split_views.loc[c].loc[row_idx, 'dicom_id']], axis=1)\n",
-    "tbl.columns = splits\n",
+    "    n_images[c] = {}\n",
+    "    for view in row_idx:\n",
+    "        n_images[c][view] = split_views.loc[c].loc[view, 'dicom_id']\n",
+    "n_images = pd.DataFrame.from_dict(n_images, orient='index')\n",
+    "n_images = n_images.T\n",
+    "\n",
+    "\n",
+    "# convert frontal/lateral/other into \"N (%)\"\n",
+    "for i in n_images.index:\n",
+    "    for c in splits:\n",
+    "        val = n_images.loc[i, c]\n",
+    "        n_images.loc[i, c] = f'{val} ({100.0*val/tbl.loc[\"Number of images\", c]:3.1f}%)'\n",
     "\n",
+    "tbl = pd.concat([tbl, n_images], axis=0, sort=False)\n",
     "\n",
     "# add in the number of subjects\n",
     "n_studies = df.groupby('split')[['study_id']].nunique().T\n",
@@ -687,25 +618,614 @@
     "# studies with a finding\n",
     "n_studies = df.loc[df['has_negbio_finding']].groupby('split')[['study_id']].nunique().T\n",
     "n_studies.index = ['  with a finding']\n",
+    "for c in splits:\n",
+    "    val = n_studies.loc['  with a finding', c]\n",
+    "    n_studies.loc['  with a finding', c] = f'{val} ({100.0*val/tbl.loc[\"Number of studies\", c]:3.1f}%)'\n",
     "tbl = pd.concat([tbl, n_studies], axis=0, sort=False)\n",
-    "n_studies.index = ['  with a finding (%)']\n",
-    "tbl = pd.concat([tbl, n_studies / tbl.iloc[-2] * 100.0], axis=0, sort=False)\n",
     "\n",
     "# patients\n",
     "n_pt = df.groupby('split')[['subject_id']].nunique().T\n",
     "n_pt.index = ['Number of patients']\n",
     "tbl = pd.concat([tbl, n_pt], axis=0, sort=False)\n",
     "\n",
+    "\n",
     "# patients with a finding\n",
     "n_studies = df.loc[df['has_negbio_finding']].groupby('split')[['subject_id']].nunique().T\n",
     "n_studies.index = ['  with a finding']\n",
+    "for c in splits:\n",
+    "    val = n_studies.loc['  with a finding', c]\n",
+    "    n_studies.loc['  with a finding', c] = f'{val} ({100.0*val/tbl.loc[\"Number of patients\", c]:3.1f}%)'\n",
     "tbl = pd.concat([tbl, n_studies], axis=0, sort=False)\n",
-    "n_studies.index = ['  with a finding (%)']\n",
-    "tbl = pd.concat([tbl, n_studies / tbl.iloc[-2] * 100.0], axis=0, sort=False)\n",
     "\n",
     "tbl.to_latex('table2.tex')\n",
+    "\n",
+    "tbl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Frequency of findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject_id</th>\n",
+       "      <th>study_id</th>\n",
+       "      <th>Atelectasis</th>\n",
+       "      <th>Cardiomegaly</th>\n",
+       "      <th>Consolidation</th>\n",
+       "      <th>Edema</th>\n",
+       "      <th>Enlarged Cardiomediastinum</th>\n",
+       "      <th>Fracture</th>\n",
+       "      <th>Lung Lesion</th>\n",
+       "      <th>Lung Opacity</th>\n",
+       "      <th>No Finding</th>\n",
+       "      <th>Pleural Effusion</th>\n",
+       "      <th>Pleural Other</th>\n",
+       "      <th>Pneumonia</th>\n",
+       "      <th>Pneumothorax</th>\n",
+       "      <th>Support Devices</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000032</td>\n",
+       "      <td>50414267</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10000032</td>\n",
+       "      <td>53189527</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10000032</td>\n",
+       "      <td>53911762</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000032</td>\n",
+       "      <td>56699142</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10000764</td>\n",
+       "      <td>57375967</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Uncertain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10000898</td>\n",
+       "      <td>50771383</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10000898</td>\n",
+       "      <td>54205396</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10000935</td>\n",
+       "      <td>50578979</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Uncertain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Disagreement</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10000935</td>\n",
+       "      <td>51178377</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Uncertain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10000935</td>\n",
+       "      <td>55697293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   subject_id  study_id Atelectasis Cardiomegaly Consolidation      Edema  \\\n",
+       "0    10000032  50414267         NaN          NaN           NaN        NaN   \n",
+       "1    10000032  53189527         NaN          NaN           NaN        NaN   \n",
+       "2    10000032  53911762         NaN          NaN           NaN        NaN   \n",
+       "3    10000032  56699142         NaN          NaN           NaN        NaN   \n",
+       "4    10000764  57375967         NaN          NaN      Positive        NaN   \n",
+       "5    10000898  50771383         NaN          NaN           NaN        NaN   \n",
+       "6    10000898  54205396         NaN          NaN           NaN        NaN   \n",
+       "7    10000935  50578979         NaN          NaN           NaN  Uncertain   \n",
+       "8    10000935  51178377         NaN          NaN           NaN        NaN   \n",
+       "9    10000935  55697293         NaN          NaN           NaN        NaN   \n",
+       "\n",
+       "  Enlarged Cardiomediastinum Fracture Lung Lesion  Lung Opacity No Finding  \\\n",
+       "0                        NaN      NaN         NaN           NaN   Positive   \n",
+       "1                        NaN      NaN         NaN           NaN   Positive   \n",
+       "2                        NaN      NaN         NaN           NaN   Positive   \n",
+       "3                        NaN      NaN         NaN           NaN   Positive   \n",
+       "4                        NaN      NaN         NaN           NaN        NaN   \n",
+       "5                        NaN      NaN         NaN           NaN   Positive   \n",
+       "6                        NaN      NaN         NaN           NaN   Positive   \n",
+       "7                        NaN      NaN         NaN  Disagreement        NaN   \n",
+       "8                        NaN      NaN         NaN      Positive        NaN   \n",
+       "9                        NaN      NaN         NaN           NaN   Positive   \n",
+       "\n",
+       "  Pleural Effusion Pleural Other  Pneumonia Pneumothorax Support Devices  \n",
+       "0              NaN           NaN        NaN          NaN             NaN  \n",
+       "1              NaN           NaN        NaN          NaN             NaN  \n",
+       "2              NaN           NaN        NaN          NaN             NaN  \n",
+       "3              NaN           NaN        NaN          NaN             NaN  \n",
+       "4              NaN           NaN  Uncertain          NaN             NaN  \n",
+       "5              NaN           NaN        NaN          NaN             NaN  \n",
+       "6              NaN           NaN        NaN          NaN             NaN  \n",
+       "7         Positive           NaN   Positive          NaN             NaN  \n",
+       "8              NaN           NaN  Uncertain          NaN             NaN  \n",
+       "9              NaN           NaN        NaN          NaN             NaN  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency of labels in MIMIC-CXR-JPG on the training subset of 222,750 unique radiologic studies.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Positive</th>\n",
+       "      <th>Negative</th>\n",
+       "      <th>Uncertain</th>\n",
+       "      <th>Disagreement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Atelectasis</th>\n",
+       "      <td>44,012 (19.8%)</td>\n",
+       "      <td>921.0 (0.4%)</td>\n",
+       "      <td>9,623.0 (4.3%)</td>\n",
+       "      <td>1,705 (0.8%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cardiomegaly</th>\n",
+       "      <td>38,002 (17.1%)</td>\n",
+       "      <td>15,563.0 (7.0%)</td>\n",
+       "      <td>5,753.0 (2.6%)</td>\n",
+       "      <td>5,769 (2.6%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Consolidation</th>\n",
+       "      <td>10,199 (4.6%)</td>\n",
+       "      <td>7,791.0 (3.5%)</td>\n",
+       "      <td>2,913.0 (1.3%)</td>\n",
+       "      <td>1,576 (0.7%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Edema</th>\n",
+       "      <td>25,549 (11.5%)</td>\n",
+       "      <td>24,746.0 (11.1%)</td>\n",
+       "      <td>11,426.0 (5.1%)</td>\n",
+       "      <td>2,282 (1.0%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Enlarged Cardiomediastinum</th>\n",
+       "      <td>6,798 (3.1%)</td>\n",
+       "      <td>5,158.0 (2.3%)</td>\n",
+       "      <td>9,015.0 (4.0%)</td>\n",
+       "      <td>248 (0.1%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fracture</th>\n",
+       "      <td>3,675 (1.6%)</td>\n",
+       "      <td>871.0 (0.4%)</td>\n",
+       "      <td>295.0 (0.1%)</td>\n",
+       "      <td>867 (0.4%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lung Lesion</th>\n",
+       "      <td>5,939 (2.7%)</td>\n",
+       "      <td>822.0 (0.4%)</td>\n",
+       "      <td>996.0 (0.4%)</td>\n",
+       "      <td>289 (0.1%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lung Opacity</th>\n",
+       "      <td>49,512 (22.2%)</td>\n",
+       "      <td>2,794.0 (1.3%)</td>\n",
+       "      <td>2,052.0 (0.9%)</td>\n",
+       "      <td>2,460 (1.1%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>No Finding</th>\n",
+       "      <td>74,019 (33.2%)</td>\n",
+       "      <td>nan (nan%)</td>\n",
+       "      <td>nan (nan%)</td>\n",
+       "      <td>3,825 (1.7%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pleural Effusion</th>\n",
+       "      <td>51,680 (23.2%)</td>\n",
+       "      <td>26,532.0 (11.9%)</td>\n",
+       "      <td>5,184.0 (2.3%)</td>\n",
+       "      <td>1,617 (0.7%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pleural Other</th>\n",
+       "      <td>1,884 (0.8%)</td>\n",
+       "      <td>120.0 (0.1%)</td>\n",
+       "      <td>707.0 (0.3%)</td>\n",
+       "      <td>91 (0.0%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pneumonia</th>\n",
+       "      <td>15,333 (6.9%)</td>\n",
+       "      <td>23,771.0 (10.7%)</td>\n",
+       "      <td>17,313.0 (7.8%)</td>\n",
+       "      <td>1,377 (0.6%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pneumothorax</th>\n",
+       "      <td>9,159 (4.1%)</td>\n",
+       "      <td>41,250.0 (18.5%)</td>\n",
+       "      <td>848.0 (0.4%)</td>\n",
+       "      <td>1,291 (0.6%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Support Devices</th>\n",
+       "      <td>63,971 (28.7%)</td>\n",
+       "      <td>3,014.0 (1.4%)</td>\n",
+       "      <td>95.0 (0.0%)</td>\n",
+       "      <td>1,785 (0.8%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Positive          Negative        Uncertain  \\\n",
+       "Atelectasis                 44,012 (19.8%)      921.0 (0.4%)   9,623.0 (4.3%)   \n",
+       "Cardiomegaly                38,002 (17.1%)   15,563.0 (7.0%)   5,753.0 (2.6%)   \n",
+       "Consolidation                10,199 (4.6%)    7,791.0 (3.5%)   2,913.0 (1.3%)   \n",
+       "Edema                       25,549 (11.5%)  24,746.0 (11.1%)  11,426.0 (5.1%)   \n",
+       "Enlarged Cardiomediastinum    6,798 (3.1%)    5,158.0 (2.3%)   9,015.0 (4.0%)   \n",
+       "Fracture                      3,675 (1.6%)      871.0 (0.4%)     295.0 (0.1%)   \n",
+       "Lung Lesion                   5,939 (2.7%)      822.0 (0.4%)     996.0 (0.4%)   \n",
+       "Lung Opacity                49,512 (22.2%)    2,794.0 (1.3%)   2,052.0 (0.9%)   \n",
+       "No Finding                  74,019 (33.2%)        nan (nan%)       nan (nan%)   \n",
+       "Pleural Effusion            51,680 (23.2%)  26,532.0 (11.9%)   5,184.0 (2.3%)   \n",
+       "Pleural Other                 1,884 (0.8%)      120.0 (0.1%)     707.0 (0.3%)   \n",
+       "Pneumonia                    15,333 (6.9%)  23,771.0 (10.7%)  17,313.0 (7.8%)   \n",
+       "Pneumothorax                  9,159 (4.1%)  41,250.0 (18.5%)     848.0 (0.4%)   \n",
+       "Support Devices             63,971 (28.7%)    3,014.0 (1.4%)      95.0 (0.0%)   \n",
+       "\n",
+       "                            Disagreement  \n",
+       "Atelectasis                 1,705 (0.8%)  \n",
+       "Cardiomegaly                5,769 (2.6%)  \n",
+       "Consolidation               1,576 (0.7%)  \n",
+       "Edema                       2,282 (1.0%)  \n",
+       "Enlarged Cardiomediastinum    248 (0.1%)  \n",
+       "Fracture                      867 (0.4%)  \n",
+       "Lung Lesion                   289 (0.1%)  \n",
+       "Lung Opacity                2,460 (1.1%)  \n",
+       "No Finding                  3,825 (1.7%)  \n",
+       "Pleural Effusion            1,617 (0.7%)  \n",
+       "Pleural Other                  91 (0.0%)  \n",
+       "Pneumonia                   1,377 (0.6%)  \n",
+       "Pneumothorax                1,291 (0.6%)  \n",
+       "Support Devices             1,785 (0.8%)  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nb = pd.read_csv(mimic_cxr_path / 'mimic-cxr-2.0.0-negbio.csv.gz')\n",
+    "cx = pd.read_csv(mimic_cxr_path / 'mimic-cxr-2.0.0-chexpert.csv.gz')\n",
+    "\n",
+    "# merge these findings to create a table\n",
+    "# both agree -> output label\n",
+    "# disagree -> output -9\n",
+    "\n",
+    "# drop subject_id from cx - we have it in nb\n",
+    "df = nb.merge(\n",
+    "    cx.drop('subject_id', axis=1),\n",
+    "    how='left',\n",
+    "    left_on='study_id', right_on='study_id',\n",
+    "    suffixes=('', '_cx')\n",
+    ")\n",
+    "\n",
+    "# subselect to training set\n",
+    "study_ids = set(df_split.loc[df_split['split']=='train', 'study_id'])\n",
+    "df = df.loc[df['study_id'].isin(study_ids)]\n",
+    "\n",
+    "# replace numeric labels with meaningful labels\n",
+    "# also annotate disagreements between the two labelers\n",
+    "labels = {0: 'Negative', 1: 'Positive', -1: 'Uncertain', -9: 'Disagreement'}\n",
+    "for c in df.columns:\n",
+    "    if c in ('subject_id', 'study_id'):\n",
+    "        continue\n",
+    "    elif c.endswith('_cx'):\n",
+    "        continue\n",
+    "    \n",
+    "    # chexpert column\n",
+    "    c_cx = f'{c}_cx'\n",
+    "    \n",
+    "    # annotate disagreement\n",
+    "    for val in labels.keys():\n",
+    "        if val == -9:\n",
+    "            continue\n",
+    "        \n",
+    "        # check one is null and the other isn't\n",
+    "        idx = df[c].isnull() & df[c_cx].notnull()\n",
+    "        df.loc[idx, c] = -9\n",
+    "        \n",
+    "        idx = df[c].notnull() & df[c_cx].isnull()\n",
+    "        df.loc[idx, c] = -9\n",
+    "        \n",
+    "        # check both non-null, but different value\n",
+    "        idx = df[c].notnull() & df[c_cx].notnull() & (df[c] != df[c_cx])\n",
+    "        df.loc[idx, c] = -9\n",
+    "        \n",
+    "    # now for those missing in negbio\n",
+    "    idx = df[c].isnull() & df[f'{c}_cx'].notnull()\n",
+    "    df.loc[idx, c] = -9\n",
+    "    \n",
+    "    df[c] = df[c].map(labels)\n",
+    "    \n",
+    "# drop chexpert columns\n",
+    "cols_drop = [c for c in df.columns if c.endswith('_cx')]\n",
+    "df.drop(cols_drop, axis=1, inplace=True)\n",
+    "\n",
+    "# display a few example cases\n",
+    "display(df.head(n=10))\n",
+    "\n",
+    "# create a summary table of the findings\n",
+    "grp_cols = [c for c in df.columns if c not in ('subject_id', 'study_id')]\n",
+    "tbl = {}\n",
+    "for c in grp_cols:\n",
+    "    tbl[c] = df[c].value_counts().to_dict()\n",
+    "tbl = pd.DataFrame.from_dict(tbl, orient='index')\n",
+    "\n",
+    "\n",
+    "# pretty format the labels\n",
+    "N = df.shape[0]\n",
+    "for c in tbl.columns:\n",
+    "    tbl[c] = tbl[c].apply(lambda x: f'{x:,} ({100.0*x/N:3.1f}%)')\n",
+    "\n",
+    "# sort columns\n",
+    "print(f'Frequency of labels in MIMIC-CXR-JPG on the training subset of {df.shape[0]:,} unique radiologic studies.')\n",
+    "tbl = tbl[['Positive', 'Negative', 'Uncertain', 'Disagreement']]\n",
+    "tbl.to_latex('findings_frequency.tex')\n",
     "tbl"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report sectioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['study', 'impression', 'findings', 'last_paragraph', 'comparison'], dtype='object')\n",
+      "Of the total 227,835 reports.. \n",
+      "  189,561 (83.2%) had a impression section\n",
+      "  27,684 (12.2%) had a findings section\n",
+      "  10,514 (4.6%) had a last_paragraph section\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(mimic_cxr_path / 'cxr-record-list.csv.gz', header=0, sep=',')\n",
+    "sections = pd.read_csv(mimic_cxr_path / 'mimic-cxr-sections/mimic_cxr_sectioned.csv')\n",
+    "\n",
+    "print(sections.columns)\n",
+    "N = df['study_id'].nunique()\n",
+    "print(f'Of the total {N:,} reports.. ')\n",
+    "idx = sections['study'].notnull()\n",
+    "for c in ['impression', 'findings', 'last_paragraph']:\n",
+    "    n = sections.loc[idx, c].count()\n",
+    "    print(f'  {n:,} ({100.0*n/N:3.1f}%) had a {c} section')\n",
+    "    # limit next check to only rows where this section is null\n",
+    "    idx = idx & sections[c].isnull()\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/txt/validation/compare_negbio_and_chexpert.ipynb b/txt/validation/compare_negbio_and_chexpert.ipynb
index 790042c..90d5931 100644
--- a/txt/validation/compare_negbio_and_chexpert.ipynb
+++ b/txt/validation/compare_negbio_and_chexpert.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
        "s1017              NaN  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,175 +363,50 @@
     "    return df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mention\n",
+    "\n",
+    "You'll note that the mention scores are approximately identical.\n",
+    "\n",
+    "* NegBio uses the CheXpert patterns for mention detection\n",
+    "* NegBio does not use the same post-processing filter for `No Finding` that CheXpert does"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NegBio No Finding:\n"
+     ]
+    },
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>positives</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>No Finding</th>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.866667</td>\n",
-       "      <td>0.530612</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cardiomegaly</th>\n",
-       "      <td>0.813688</td>\n",
-       "      <td>0.910638</td>\n",
-       "      <td>0.859438</td>\n",
-       "      <td>235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Lesion</th>\n",
-       "      <td>0.861538</td>\n",
-       "      <td>0.848485</td>\n",
-       "      <td>0.854962</td>\n",
-       "      <td>66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Opacity</th>\n",
-       "      <td>0.715447</td>\n",
-       "      <td>0.907216</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Edema</th>\n",
-       "      <td>0.799296</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.888454</td>\n",
-       "      <td>227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Consolidation</th>\n",
-       "      <td>0.885714</td>\n",
-       "      <td>0.978947</td>\n",
-       "      <td>0.930000</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumonia</th>\n",
-       "      <td>0.928270</td>\n",
-       "      <td>0.986547</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Atelectasis</th>\n",
-       "      <td>0.893443</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.943723</td>\n",
-       "      <td>218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumothorax</th>\n",
-       "      <td>0.945378</td>\n",
-       "      <td>0.995575</td>\n",
-       "      <td>0.969828</td>\n",
-       "      <td>226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Effusion</th>\n",
-       "      <td>0.967914</td>\n",
-       "      <td>0.978378</td>\n",
-       "      <td>0.973118</td>\n",
-       "      <td>370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Other</th>\n",
-       "      <td>0.477273</td>\n",
-       "      <td>0.777778</td>\n",
-       "      <td>0.591549</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fracture</th>\n",
-       "      <td>0.870370</td>\n",
-       "      <td>0.940000</td>\n",
-       "      <td>0.903846</td>\n",
-       "      <td>50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Support Devices</th>\n",
-       "      <td>0.823105</td>\n",
-       "      <td>0.974359</td>\n",
-       "      <td>0.892368</td>\n",
-       "      <td>234</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                   0.382353  0.866667  0.530612         30\n",
-       "Enlarged Cardiomediastinum   0.375000  0.600000  0.461538         70\n",
-       "Cardiomegaly                 0.813688  0.910638  0.859438        235\n",
-       "Lung Lesion                  0.861538  0.848485  0.854962         66\n",
-       "Lung Opacity                 0.715447  0.907216  0.800000        194\n",
-       "Edema                        0.799296  1.000000  0.888454        227\n",
-       "Consolidation                0.885714  0.978947  0.930000         95\n",
-       "Pneumonia                    0.928270  0.986547  0.956522        223\n",
-       "Atelectasis                  0.893443  1.000000  0.943723        218\n",
-       "Pneumothorax                 0.945378  0.995575  0.969828        226\n",
-       "Pleural Effusion             0.967914  0.978378  0.973118        370\n",
-       "Pleural Other                0.477273  0.777778  0.591549         27\n",
-       "Fracture                     0.870370  0.940000  0.903846         50\n",
-       "Support Devices              0.823105  0.974359  0.892368        234"
+       "precision     0.382353\n",
+       "recall        0.866667\n",
+       "f1            0.530612\n",
+       "positives    30.000000\n",
+       "Name: No Finding, dtype: float64"
       ]
      },
-     "execution_count": 21,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = evaluate_labels(gs, df_nih, method='mention')\n",
-    "df.to_latex('nih_mention.tex')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CheXpert mention:\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -562,100 +437,100 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>No Finding</th>\n",
-       "      <td>0.403226</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.543478</td>\n",
+       "      <td>0.403</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.543</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.461538</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.462</td>\n",
        "      <td>70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cardiomegaly</th>\n",
-       "      <td>0.813688</td>\n",
-       "      <td>0.910638</td>\n",
-       "      <td>0.859438</td>\n",
+       "      <td>0.814</td>\n",
+       "      <td>0.911</td>\n",
+       "      <td>0.859</td>\n",
        "      <td>235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lung Lesion</th>\n",
-       "      <td>0.861538</td>\n",
-       "      <td>0.848485</td>\n",
-       "      <td>0.854962</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.855</td>\n",
        "      <td>66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lung Opacity</th>\n",
-       "      <td>0.715447</td>\n",
-       "      <td>0.907216</td>\n",
-       "      <td>0.800000</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.800</td>\n",
        "      <td>194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Edema</th>\n",
-       "      <td>0.799296</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.888454</td>\n",
+       "      <td>0.799</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.888</td>\n",
        "      <td>227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Consolidation</th>\n",
-       "      <td>0.885714</td>\n",
-       "      <td>0.978947</td>\n",
-       "      <td>0.930000</td>\n",
+       "      <td>0.886</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>0.930</td>\n",
        "      <td>95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumonia</th>\n",
-       "      <td>0.928270</td>\n",
-       "      <td>0.986547</td>\n",
-       "      <td>0.956522</td>\n",
+       "      <td>0.928</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.957</td>\n",
        "      <td>223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Atelectasis</th>\n",
-       "      <td>0.893443</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.943723</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.944</td>\n",
        "      <td>218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumothorax</th>\n",
-       "      <td>0.945378</td>\n",
-       "      <td>0.995575</td>\n",
-       "      <td>0.969828</td>\n",
+       "      <td>0.945</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.970</td>\n",
        "      <td>226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pleural Effusion</th>\n",
-       "      <td>0.967914</td>\n",
-       "      <td>0.978378</td>\n",
-       "      <td>0.973118</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.978</td>\n",
+       "      <td>0.973</td>\n",
        "      <td>370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pleural Other</th>\n",
-       "      <td>0.477273</td>\n",
-       "      <td>0.777778</td>\n",
-       "      <td>0.591549</td>\n",
+       "      <td>0.477</td>\n",
+       "      <td>0.778</td>\n",
+       "      <td>0.592</td>\n",
        "      <td>27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fracture</th>\n",
-       "      <td>0.870370</td>\n",
-       "      <td>0.940000</td>\n",
-       "      <td>0.903846</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>0.940</td>\n",
+       "      <td>0.904</td>\n",
        "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Support Devices</th>\n",
-       "      <td>0.804965</td>\n",
-       "      <td>0.970085</td>\n",
-       "      <td>0.879845</td>\n",
+       "      <td>0.805</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.880</td>\n",
        "      <td>234</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -663,215 +538,62 @@
        "</div>"
       ],
       "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                   0.403226  0.833333  0.543478         30\n",
-       "Enlarged Cardiomediastinum   0.375000  0.600000  0.461538         70\n",
-       "Cardiomegaly                 0.813688  0.910638  0.859438        235\n",
-       "Lung Lesion                  0.861538  0.848485  0.854962         66\n",
-       "Lung Opacity                 0.715447  0.907216  0.800000        194\n",
-       "Edema                        0.799296  1.000000  0.888454        227\n",
-       "Consolidation                0.885714  0.978947  0.930000         95\n",
-       "Pneumonia                    0.928270  0.986547  0.956522        223\n",
-       "Atelectasis                  0.893443  1.000000  0.943723        218\n",
-       "Pneumothorax                 0.945378  0.995575  0.969828        226\n",
-       "Pleural Effusion             0.967914  0.978378  0.973118        370\n",
-       "Pleural Other                0.477273  0.777778  0.591549         27\n",
-       "Fracture                     0.870370  0.940000  0.903846         50\n",
-       "Support Devices              0.804965  0.970085  0.879845        234"
+       "                            precision  recall     f1  positives\n",
+       "No Finding                      0.403   0.833  0.543         30\n",
+       "Enlarged Cardiomediastinum      0.375   0.600  0.462         70\n",
+       "Cardiomegaly                    0.814   0.911  0.859        235\n",
+       "Lung Lesion                     0.862   0.848  0.855         66\n",
+       "Lung Opacity                    0.715   0.907  0.800        194\n",
+       "Edema                           0.799   1.000  0.888        227\n",
+       "Consolidation                   0.886   0.979  0.930         95\n",
+       "Pneumonia                       0.928   0.987  0.957        223\n",
+       "Atelectasis                     0.893   1.000  0.944        218\n",
+       "Pneumothorax                    0.945   0.996  0.970        226\n",
+       "Pleural Effusion                0.968   0.978  0.973        370\n",
+       "Pleural Other                   0.477   0.778  0.592         27\n",
+       "Fracture                        0.870   0.940  0.904         50\n",
+       "Support Devices                 0.805   0.970  0.880        234"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "\n",
+    "df = evaluate_labels(gs, df_nih, method='mention')\n",
+    "print('NegBio No Finding:')\n",
+    "display(df.loc['No Finding'])\n",
+    "\n",
+    "print('CheXpert mention:')\n",
     "df = evaluate_labels(gs, df_chexpert, method='mention')\n",
-    "df.to_latex('chexpert_mention.tex')\n",
+    "\n",
+    "for c in df.columns:\n",
+    "    if 'float' in str(df.dtypes[c]):\n",
+    "        df[c] = np.round(df[c], 3)\n",
     "df"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 24,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alistairewj/miniconda3/envs/mimic-cxr/lib/python3.6/site-packages/ipykernel_launcher.py:24: RuntimeWarning: invalid value encountered in double_scalars\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>positives</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>No Finding</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.033333</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.064516</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cardiomegaly</th>\n",
-       "      <td>0.155556</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.237288</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Lesion</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Opacity</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Edema</th>\n",
-       "      <td>0.102041</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.169492</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Consolidation</th>\n",
-       "      <td>0.529412</td>\n",
-       "      <td>0.529412</td>\n",
-       "      <td>0.529412</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumonia</th>\n",
-       "      <td>0.431818</td>\n",
-       "      <td>0.612903</td>\n",
-       "      <td>0.506667</td>\n",
-       "      <td>62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Atelectasis</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.705882</td>\n",
-       "      <td>0.452830</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumothorax</th>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Effusion</th>\n",
-       "      <td>0.432432</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>0.581818</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Other</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fracture</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Support Devices</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                        NaN       NaN       NaN          0\n",
-       "Enlarged Cardiomediastinum   0.033333  1.000000  0.064516          1\n",
-       "Cardiomegaly                 0.155556  0.500000  0.237288         14\n",
-       "Lung Lesion                  0.000000  0.000000       NaN          8\n",
-       "Lung Opacity                      NaN       NaN       NaN          0\n",
-       "Edema                        0.102041  0.500000  0.169492         10\n",
-       "Consolidation                0.529412  0.529412  0.529412         17\n",
-       "Pneumonia                    0.431818  0.612903  0.506667         62\n",
-       "Atelectasis                  0.333333  0.705882  0.452830         17\n",
-       "Pneumothorax                 0.375000  0.375000  0.375000          8\n",
-       "Pleural Effusion             0.432432  0.888889  0.581818         18\n",
-       "Pleural Other                     NaN       NaN       NaN          0\n",
-       "Fracture                     0.500000  0.500000  0.500000          2\n",
-       "Support Devices                   NaN       NaN       NaN          0"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df = evaluate_labels(gs, df_nih, method='uncertain')\n",
-    "df.to_latex('nih_uncertain.tex')\n",
-    "df"
+    "# Uncertain"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/alistairewj/miniconda3/envs/mimic-cxr/lib/python3.6/site-packages/ipykernel_launcher.py:24: RuntimeWarning: invalid value encountered in double_scalars\n"
+      "/home/alistairewj/miniconda3/envs/mimic-cxr/lib/python3.6/site-packages/ipykernel_launcher.py:24: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "/home/alistairewj/miniconda3/envs/mimic-cxr/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
      ]
     },
     {
@@ -887,19 +609,43 @@
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
-       "    .dataframe thead th {\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
+       "    <tr>\n",
        "      <th></th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
        "      <th>positives</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uncertainty</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -907,26 +653,38 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.035714</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.068966</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.069</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cardiomegaly</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.156</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.237</td>\n",
        "      <td>NaN</td>\n",
        "      <td>14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lung Lesion</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
@@ -935,48 +693,69 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Edema</th>\n",
-       "      <td>0.125000</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.206897</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.207</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Consolidation</th>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>0.176471</td>\n",
-       "      <td>0.214286</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>0.273</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>0.176</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>0.214</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumonia</th>\n",
-       "      <td>0.406977</td>\n",
-       "      <td>0.564516</td>\n",
-       "      <td>0.472973</td>\n",
+       "      <td>0.432</td>\n",
+       "      <td>0.407</td>\n",
+       "      <td>0.613</td>\n",
+       "      <td>0.565</td>\n",
+       "      <td>0.507</td>\n",
+       "      <td>0.473</td>\n",
        "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Atelectasis</th>\n",
-       "      <td>0.289474</td>\n",
-       "      <td>0.647059</td>\n",
-       "      <td>0.400000</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.289</td>\n",
+       "      <td>0.706</td>\n",
+       "      <td>0.647</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>0.400</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumothorax</th>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.125000</td>\n",
-       "      <td>0.166667</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.167</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pleural Effusion</th>\n",
-       "      <td>0.413793</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.510638</td>\n",
+       "      <td>0.432</td>\n",
+       "      <td>0.414</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.582</td>\n",
+       "      <td>0.511</td>\n",
        "      <td>18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -984,12 +763,18 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fracture</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.500</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
@@ -998,6 +783,9 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1005,208 +793,88 @@
        "</div>"
       ],
       "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                        NaN       NaN       NaN          0\n",
-       "Enlarged Cardiomediastinum   0.035714  1.000000  0.068966          1\n",
-       "Cardiomegaly                 0.000000  0.000000       NaN         14\n",
-       "Lung Lesion                  0.000000  0.000000       NaN          8\n",
-       "Lung Opacity                      NaN       NaN       NaN          0\n",
-       "Edema                        0.125000  0.600000  0.206897         10\n",
-       "Consolidation                0.272727  0.176471  0.214286         17\n",
-       "Pneumonia                    0.406977  0.564516  0.472973         62\n",
-       "Atelectasis                  0.289474  0.647059  0.400000         17\n",
-       "Pneumothorax                 0.250000  0.125000  0.166667          8\n",
-       "Pleural Effusion             0.413793  0.666667  0.510638         18\n",
-       "Pleural Other                     NaN       NaN       NaN          0\n",
-       "Fracture                     0.000000  0.000000       NaN          2\n",
-       "Support Devices                   NaN       NaN       NaN          0"
+       "                           precision          recall              f1           \\\n",
+       "                              NegBio CheXpert NegBio CheXpert NegBio CheXpert   \n",
+       "Uncertainty                                                                     \n",
+       "No Finding                       NaN      NaN    NaN      NaN    NaN      NaN   \n",
+       "Enlarged Cardiomediastinum     0.033    0.036  1.000    1.000  0.065    0.069   \n",
+       "Cardiomegaly                   0.156    0.000  0.500    0.000  0.237      NaN   \n",
+       "Lung Lesion                    0.000    0.000  0.000    0.000    NaN      NaN   \n",
+       "Lung Opacity                     NaN      NaN    NaN      NaN    NaN      NaN   \n",
+       "Edema                          0.102    0.125  0.500    0.600  0.169    0.207   \n",
+       "Consolidation                  0.529    0.273  0.529    0.176  0.529    0.214   \n",
+       "Pneumonia                      0.432    0.407  0.613    0.565  0.507    0.473   \n",
+       "Atelectasis                    0.333    0.289  0.706    0.647  0.453    0.400   \n",
+       "Pneumothorax                   0.375    0.250  0.375    0.125  0.375    0.167   \n",
+       "Pleural Effusion               0.432    0.414  0.889    0.667  0.582    0.511   \n",
+       "Pleural Other                    NaN      NaN    NaN      NaN    NaN      NaN   \n",
+       "Fracture                       0.500    0.000  0.500    0.000  0.500      NaN   \n",
+       "Support Devices                  NaN      NaN    NaN      NaN    NaN      NaN   \n",
+       "\n",
+       "                           positives  \n",
+       "                              NegBio  \n",
+       "Uncertainty                           \n",
+       "No Finding                         0  \n",
+       "Enlarged Cardiomediastinum         1  \n",
+       "Cardiomegaly                      14  \n",
+       "Lung Lesion                        8  \n",
+       "Lung Opacity                       0  \n",
+       "Edema                             10  \n",
+       "Consolidation                     17  \n",
+       "Pneumonia                         62  \n",
+       "Atelectasis                       17  \n",
+       "Pneumothorax                       8  \n",
+       "Pleural Effusion                  18  \n",
+       "Pleural Other                      0  \n",
+       "Fracture                           2  \n",
+       "Support Devices                    0  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = evaluate_labels(gs, df_chexpert, method='uncertain')\n",
-    "df.to_latex('chexpert_uncertain.tex')\n",
+    "df = evaluate_labels(gs, df_nih, method='uncertain')\n",
+    "df.columns = pd.MultiIndex.from_tuples([('NegBio', c) for c in df.columns])\n",
+    "\n",
+    "cx = evaluate_labels(gs, df_chexpert, method='uncertain')\n",
+    "cx.columns = pd.MultiIndex.from_tuples([('CheXpert', c) for c in cx.columns])\n",
+    "\n",
+    "df = df.merge(cx, how='inner', left_index=True, right_index=True)\n",
+    "# df.columns.swaplabel(0, 1, axis=1, inplace=True)\n",
+    "df.columns = df.columns.reorder_levels([1, 0])\n",
+    "\n",
+    "# re-order columns\n",
+    "df = df[['precision', 'recall', 'f1', 'positives']]\n",
+    "\n",
+    "# round values\n",
+    "for c in df.columns:\n",
+    "    if 'float' in str(df.dtypes[c]):\n",
+    "        df[c] = np.round(df[c], 3)\n",
+    "\n",
+    "# drop the unecessary final column\n",
+    "df.drop(('positives', 'CheXpert'), axis=1, inplace=True)\n",
+    "\n",
+    "# output to latex\n",
+    "df.index.name = 'Uncertainty'\n",
+    "\n",
+    "df.to_latex('uncertainty.tex')\n",
+    "\n",
     "df"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 26,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alistairewj/miniconda3/envs/mimic-cxr/lib/python3.6/site-packages/ipykernel_launcher.py:24: RuntimeWarning: invalid value encountered in double_scalars\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>positives</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>No Finding</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.653846</td>\n",
-       "      <td>0.607143</td>\n",
-       "      <td>0.629630</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Cardiomegaly</th>\n",
-       "      <td>0.855072</td>\n",
-       "      <td>0.719512</td>\n",
-       "      <td>0.781457</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Lesion</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lung Opacity</th>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.391304</td>\n",
-       "      <td>0.409091</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Edema</th>\n",
-       "      <td>0.712871</td>\n",
-       "      <td>0.847059</td>\n",
-       "      <td>0.774194</td>\n",
-       "      <td>85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Consolidation</th>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>0.936170</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumonia</th>\n",
-       "      <td>0.835616</td>\n",
-       "      <td>0.734940</td>\n",
-       "      <td>0.782051</td>\n",
-       "      <td>83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Atelectasis</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pneumothorax</th>\n",
-       "      <td>0.919355</td>\n",
-       "      <td>0.955307</td>\n",
-       "      <td>0.936986</td>\n",
-       "      <td>179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Effusion</th>\n",
-       "      <td>0.905882</td>\n",
-       "      <td>0.939024</td>\n",
-       "      <td>0.922156</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pleural Other</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fracture</th>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Support Devices</th>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.266667</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                        NaN       NaN       NaN          0\n",
-       "Enlarged Cardiomediastinum   0.653846  0.607143  0.629630         28\n",
-       "Cardiomegaly                 0.855072  0.719512  0.781457         82\n",
-       "Lung Lesion                  0.500000  0.500000  0.500000          4\n",
-       "Lung Opacity                 0.428571  0.391304  0.409091         23\n",
-       "Edema                        0.712871  0.847059  0.774194         85\n",
-       "Consolidation                0.916667  0.956522  0.936170         23\n",
-       "Pneumonia                    0.835616  0.734940  0.782051         83\n",
-       "Atelectasis                  0.333333  0.750000  0.461538          4\n",
-       "Pneumothorax                 0.919355  0.955307  0.936986        179\n",
-       "Pleural Effusion             0.905882  0.939024  0.922156         82\n",
-       "Pleural Other                0.000000  0.000000       NaN          2\n",
-       "Fracture                     0.600000  0.375000  0.461538          8\n",
-       "Support Devices              0.200000  0.400000  0.266667          5"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df = evaluate_labels(gs, df_nih, method='negation')\n",
-    "df.to_latex('nih_negation.tex')\n",
-    "df"
+    "# Negation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1229,19 +897,43 @@
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
-       "    .dataframe thead th {\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
+       "    <tr>\n",
        "      <th></th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
        "      <th>positives</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "      <th>CheXpert</th>\n",
+       "      <th>NegBio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Negation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1249,96 +941,138 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enlarged Cardiomediastinum</th>\n",
-       "      <td>0.653846</td>\n",
-       "      <td>0.607143</td>\n",
-       "      <td>0.629630</td>\n",
+       "      <td>0.654</td>\n",
+       "      <td>0.654</td>\n",
+       "      <td>0.607</td>\n",
+       "      <td>0.607</td>\n",
+       "      <td>0.630</td>\n",
+       "      <td>0.630</td>\n",
        "      <td>28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cardiomegaly</th>\n",
-       "      <td>0.855072</td>\n",
-       "      <td>0.719512</td>\n",
-       "      <td>0.781457</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>0.720</td>\n",
+       "      <td>0.720</td>\n",
+       "      <td>0.781</td>\n",
+       "      <td>0.781</td>\n",
        "      <td>82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lung Lesion</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lung Opacity</th>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.421053</td>\n",
+       "      <td>0.429</td>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>0.409</td>\n",
+       "      <td>0.421</td>\n",
        "      <td>23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Edema</th>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.823529</td>\n",
-       "      <td>0.765027</td>\n",
+       "      <td>0.713</td>\n",
+       "      <td>0.714</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.774</td>\n",
+       "      <td>0.765</td>\n",
        "      <td>85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Consolidation</th>\n",
-       "      <td>0.916667</td>\n",
-       "      <td>0.956522</td>\n",
-       "      <td>0.936170</td>\n",
+       "      <td>0.917</td>\n",
+       "      <td>0.917</td>\n",
+       "      <td>0.957</td>\n",
+       "      <td>0.957</td>\n",
+       "      <td>0.936</td>\n",
+       "      <td>0.936</td>\n",
        "      <td>23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumonia</th>\n",
-       "      <td>0.867647</td>\n",
-       "      <td>0.710843</td>\n",
-       "      <td>0.781457</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.781</td>\n",
        "      <td>83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Atelectasis</th>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.428571</td>\n",
+       "      <td>0.333</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.462</td>\n",
+       "      <td>0.429</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pneumothorax</th>\n",
-       "      <td>0.926136</td>\n",
-       "      <td>0.910615</td>\n",
-       "      <td>0.918310</td>\n",
+       "      <td>0.919</td>\n",
+       "      <td>0.926</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.911</td>\n",
+       "      <td>0.937</td>\n",
+       "      <td>0.918</td>\n",
        "      <td>179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pleural Effusion</th>\n",
-       "      <td>0.918605</td>\n",
-       "      <td>0.963415</td>\n",
-       "      <td>0.940476</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.919</td>\n",
+       "      <td>0.939</td>\n",
+       "      <td>0.963</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>0.940</td>\n",
        "      <td>82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pleural Other</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fracture</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.462</td>\n",
        "      <td>NaN</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Support Devices</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.200</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.400</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.267</td>\n",
        "      <td>NaN</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -1347,31 +1081,75 @@
        "</div>"
       ],
       "text/plain": [
-       "                            precision    recall        f1  positives\n",
-       "No Finding                        NaN       NaN       NaN          0\n",
-       "Enlarged Cardiomediastinum   0.653846  0.607143  0.629630         28\n",
-       "Cardiomegaly                 0.855072  0.719512  0.781457         82\n",
-       "Lung Lesion                  0.500000  0.500000  0.500000          4\n",
-       "Lung Opacity                 0.533333  0.347826  0.421053         23\n",
-       "Edema                        0.714286  0.823529  0.765027         85\n",
-       "Consolidation                0.916667  0.956522  0.936170         23\n",
-       "Pneumonia                    0.867647  0.710843  0.781457         83\n",
-       "Atelectasis                  0.300000  0.750000  0.428571          4\n",
-       "Pneumothorax                 0.926136  0.910615  0.918310        179\n",
-       "Pleural Effusion             0.918605  0.963415  0.940476         82\n",
-       "Pleural Other                0.000000  0.000000       NaN          2\n",
-       "Fracture                     0.000000  0.000000       NaN          8\n",
-       "Support Devices              0.000000  0.000000       NaN          5"
+       "                           precision          recall              f1           \\\n",
+       "                              NegBio CheXpert NegBio CheXpert NegBio CheXpert   \n",
+       "Negation                                                                        \n",
+       "No Finding                       NaN      NaN    NaN      NaN    NaN      NaN   \n",
+       "Enlarged Cardiomediastinum     0.654    0.654  0.607    0.607  0.630    0.630   \n",
+       "Cardiomegaly                   0.855    0.855  0.720    0.720  0.781    0.781   \n",
+       "Lung Lesion                    0.500    0.500  0.500    0.500  0.500    0.500   \n",
+       "Lung Opacity                   0.429    0.533  0.391    0.348  0.409    0.421   \n",
+       "Edema                          0.713    0.714  0.847    0.824  0.774    0.765   \n",
+       "Consolidation                  0.917    0.917  0.957    0.957  0.936    0.936   \n",
+       "Pneumonia                      0.836    0.868  0.735    0.711  0.782    0.781   \n",
+       "Atelectasis                    0.333    0.300  0.750    0.750  0.462    0.429   \n",
+       "Pneumothorax                   0.919    0.926  0.955    0.911  0.937    0.918   \n",
+       "Pleural Effusion               0.906    0.919  0.939    0.963  0.922    0.940   \n",
+       "Pleural Other                  0.000    0.000  0.000    0.000    NaN      NaN   \n",
+       "Fracture                       0.600    0.000  0.375    0.000  0.462      NaN   \n",
+       "Support Devices                0.200    0.000  0.400    0.000  0.267      NaN   \n",
+       "\n",
+       "                           positives  \n",
+       "                              NegBio  \n",
+       "Negation                              \n",
+       "No Finding                         0  \n",
+       "Enlarged Cardiomediastinum        28  \n",
+       "Cardiomegaly                      82  \n",
+       "Lung Lesion                        4  \n",
+       "Lung Opacity                      23  \n",
+       "Edema                             85  \n",
+       "Consolidation                     23  \n",
+       "Pneumonia                         83  \n",
+       "Atelectasis                        4  \n",
+       "Pneumothorax                     179  \n",
+       "Pleural Effusion                  82  \n",
+       "Pleural Other                      2  \n",
+       "Fracture                           8  \n",
+       "Support Devices                    5  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = evaluate_labels(gs, df_chexpert, method='negation')\n",
-    "df.to_latex('chexpert_negation.tex')\n",
+    "df = evaluate_labels(gs, df_nih, method='negation')\n",
+    "df.columns = pd.MultiIndex.from_tuples([('NegBio', c) for c in df.columns])\n",
+    "\n",
+    "cx = evaluate_labels(gs, df_chexpert, method='negation')\n",
+    "cx.columns = pd.MultiIndex.from_tuples([('CheXpert', c) for c in cx.columns])\n",
+    "\n",
+    "df = df.merge(cx, how='inner', left_index=True, right_index=True)\n",
+    "# df.columns.swaplabel(0, 1, axis=1, inplace=True)\n",
+    "df.columns = df.columns.reorder_levels([1, 0])\n",
+    "\n",
+    "# re-order columns\n",
+    "df = df[['precision', 'recall', 'f1', 'positives']]\n",
+    "\n",
+    "# round values\n",
+    "for c in df.columns:\n",
+    "    if 'float' in str(df.dtypes[c]):\n",
+    "        df[c] = np.round(df[c], 3)\n",
+    "\n",
+    "# drop the unecessary final column (redundant)\n",
+    "df.drop(('positives', 'CheXpert'), axis=1, inplace=True)\n",
+    "\n",
+    "# output to latex\n",
+    "df.index.name = 'Negation'\n",
+    "\n",
+    "df.to_latex('negation.tex')\n",
+    "\n",
     "df"
    ]
   }