01_NLP_Annotations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please take this quiz on pneumonia:\n",
    "https://goo.gl/forms/OsySYLhPclplK4hw1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import os\n",
    "import codecs\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "import sklearn.metrics\n",
    "\n",
    "# packages for interaction\n",
    "from IPython.html.widgets import interact, interactive, fixed\n",
    "from IPython.display import display, HTML, Image\n",
    "import ipywidgets\n",
    "\n",
    "# and also our utilities for this class\n",
    "from nlp_pneumonia_utils import Annotation\n",
    "from nlp_pneumonia_utils import AnnotatedDocument\n",
    "from nlp_pneumonia_utils import read_brat_annotations\n",
    "from nlp_pneumonia_utils import read_doc_annotations\n",
    "from nlp_pneumonia_utils import read_annotations\n",
    "from nlp_pneumonia_utils import calculate_prediction_metrics\n",
    "from nlp_pneumonia_utils import mark_text_custom\n",
    "from nlp_pneumonia_utils import clearPyConTextRegularExpressions\n",
    "from nlp_pneumonia_utils import pneumonia_annotation_html_markup\n",
    "print('Loaded utilities...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First, we'll load in our dataset but throughout these notebooks, there are a lot of utility functions used.  Feel free to look at them here : nlp_pneumonia_utils.py "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# First thing, let's load our training set\n",
    "annotated_doc_map = read_doc_annotations('data/training_v2.zip')\n",
    "annotated_docs = list(annotated_doc_map.values())\n",
    "print('Total Annotated Documents : {0}'.format(len(annotated_docs)))\n",
    "\n",
    "total_positives = 0\n",
    "for anno_doc in annotated_docs:\n",
    "    if anno_doc.positive_label:\n",
    "        total_positives += 1\n",
    "    \n",
    "print('Total Positive Pneumonia Documents : {0}'.format(total_positives))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next, let's look at some of the annotations annotated by our expert.  Note that there are 3 total annotation types in this set : \n",
    "1. **PNEUMONIA_DOC_YES* -> Document shows **active** or **possible** case of pneumonia\n",
    "2. **PNEUMONIA_DOC_NO** -> Document shows **no evidence** of pneumonia\n",
    "3. **EVIDENCE_OF_PNEUMONIA** -> Spans of phrases/sentence which show positive or possible evidence of pneumonia which led the expert annotator to the final document-level conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's render one of our annotated documents in HTML.  When using the function 'pneumonia_annotation_html_markup' these show up as the colors:\n",
    "1. **PNEUMONIA_DOC_YES** -> RED\n",
    "2. **PNEUMONIA_DOC_NO** -> GREEN\n",
    "3. **EVIDENCE_OF_PNEUMONIA** -> BLUE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can also use widgets to \"scrub\" through the documents and examine any of the  expert annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# This function let's us iterate through all documents and view the markup\n",
    "def view_annotation_markup(anno_docs):\n",
    "    @interact(i=ipywidgets.IntSlider(min=0, max=len(anno_docs)-1))\n",
    "    def _view_markup(i):\n",
    "        report_html = pneumonia_annotation_html_markup(anno_docs[i])\n",
    "        report_html = report_html.replace('\\n', '<br>')\n",
    "        display(HTML(report_html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "view_annotation_markup(annotated_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "source": [
    "<br/><br/>This material presented as part of the DeCART Data Science for the Health Science Summer Program at the University of Utah in 2019.<br/>\n",
    "Presenters : Dr. Wendy Chapman, Kelly Peterson, Alec Chapman, Jianlin Shi <br> Acknowledgement: Many thanks to Olga Patterson because part of the materials are adopted from his previous work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}