pyConTextNLP 0.6.0.0

A Python implementation of the ConText algorithm

# pyConTextNLP

pyConTextNLP is a Python implementation/extension/modification of the ConText algorithm described in [CITE]() which is itself a generalization of the NegEx algorithm described in [CITE]().

The package is maintained by Brian Chapman at the University of Utah. Other active and past developers include:

* Wendy W. Chapman
* Glenn Dayton


## Introduction


pyConTextNLP is a partial implementation of the ConText algorithm using Python. The original description of pyConTextNLP was provided in Chapman BE, Lee S, Kang HP, Chapman WW, "Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm." [J Biomed Inform. 2011 Oct;44(5):728-37](http://www.sciencedirect.com/science/article/pii/S1532046411000621)


Other publications/presentations based on pyConText include:
* Wilson RA, et al. "Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports." J Pathol Inform. 2010 Oct 11;1:24.
* Chapman BE, Lee S, Kang HP, Chapman WW. Using ConText to Identify Candidate Pulmonary Embolism Subjects Based on Dictated Radiology Reports. (Presented at AMIA Clinical Research Informatics Summit 2011)
* Wilson RA, Chapman WW, DeFries SJ, Becich MJ, Chapman BE. Identifying History of Ancillary Cancers in Mesothelioma Patients from Free-Text Clinical Reports. (Presented at AMIA 2010).

Note: we changed the package name from pyConText to pyConTextNLP because of a name conflict on pypi.

## Installation

pyConTextNLP can be downloaded from the Downloads page here on the negex Google Code project. Alternatively, it can be downloaded from the pypi repository http://pypi.python.org/pypi/pyConTextNLP. Since pyConTextNLP is registered with pypi, it can be installed with easy_install or pip:

easy_install pyConTextNLP
pip install pyConTextNLP

The only listed dependency is NetworkX and easy_install should also install this for you, if it is not already installed. However, there is optional functionality that is dependent on pygraphviz. I do not yet have this worked into the setuptools script.

## Code Structure

The original code used in the JBI is in the top level pyConTextNLP package. A simplification of this original algorithm that uses [http://networkx.lanl.gov/ NetworkX] is in the subpackage pyConTextNLP.pyConTextGraph. pyConTextGraph is what is currently being developed by us and is what is described here.

The package has three files:

* *itemData.py*. This is where the essential domain knowledge is stored in 4-tuples as described in the paper. For a new application, this is where the user will encapsulate the domain knowledge for their application.
* *pyConTextGraph.py*. This module defines the algorithm
* *pyConTextSql.py*.

## How to Use

I am working on improving the documentation and (hopefully) adding some testing to the code.

Some preliminary comments:

* pyConTextNLP works marks up text on a sentence by sentence level.
* pyConTextNLP facilitates reasoning from multi-sentence documents, but the markup (e.g. negation is all limited within the scope of a sentence.
* pyConTextNLP assumes the sentence is a string not a list of words

### The Skeleton of an Example

To illustrate how to use pyConTextNLP, i've taken some code excerpts from a simple application that was written to identify critical finders in radiology reports.

The first step in building an application is to define _itemData_ objects for your problem. The package contains _itemData_ objects defined in pyConTextNLP.pyConTextGraph.itemData. Common negation terms, conjunctions, pseudo-negations, etc. are defined in here. An itemData instance consists of a 4-tuple. Here is an excerpt

~~~~~

probableNegations = itemData(
["can rule out","PROBABLE_NEGATED_EXISTENCE","","forward"],
["cannot be excluded","PROBABLE_NEGATED_EXISTENCE",r"""cannot\sbe\s((entirely|completely)\s)?(excluded|ruled out)""","backward"])
~~~~~~

The four parts are
1. The _literal_ "can rule out", "cannot be excluded"
2. The _Category_ "PROBABLE_NEGATED_EXISTENCE"
3. An optional regular expression used to capture the literal in the text. If no regular expression is provided, a regular expression is generated literally from the literal.
4. An optional rule. If the itemData is being used as a modifier, the rule states what direction the modifier operates in the sentence: current valid values are: "forward", the item can modify objects following it in the sentence; "backward", the item can modify objects preceding it in the sentence; or "bidirectional", the item can modify objects preceding and following it in the sentence.

For the criticalFinderGraph.py application, we defined _itemData_ for the critical findings we wanted to identify in the text, for example pulmonary emboli and aortic dissections. These new _itemData_ objects were defined in a file named critfindingItemData.py

~~~~~
critItems = itemData(
['pulmonary embolism','PULMONARY_EMBOLISM',r'''pulmonary\s(artery )?(embol[a-z]+)''',''],
['pe','PULMONARY_EMBOLISM',r'''\bpe\b''',''],
['embolism','PULMONARY_EMBOLISM',r'''\b(emboli|embolism|embolus)\b''',''],
['aortic dissection','AORTIC_DISSECTION','',''])
~~~~~~

We also added negation terms that were not originally defined in pyConTextNLP:

~~~~
definiteNegations.prepend([["nor","DEFINITE_NEGATED_EXISTENCE","","forward"],])
~~~~~

Once we have all our _itemData_ defined, we're now ready to start processing text.

In our application we need to import the relevant modules from pyConTextNLP as well as our own _itemData_ definitions:

~~~~
import pyConTextNLP.pyConTextGraph.pyConTextGraph as pyConText
import pyConText.helpers as helpers
from critfindingItemData import *
~~~~~

Assuming we have read in our documents to process and that the basic document unit is a _report_ we can write a simple function to process the report

~~~~~
def analyzeReport(report, targets, modifiers ):
"""given an individual radiology report, markup the report based on targets and modifiers"""
# create the pyConText instance
context = pyConText.pyConText()

# split the report into individual sentences. Note this is a very simple sentence splitter. You probably
# want to write your own or use a sentence splitter from nltk or the like.
sentences = helpers.sentenceSplitter(report)

# process each sentence in the report
for s in sentences:
context.setTxt(s)
context.markItems(modifiers, mode="modifier")
context.markItems(targets, mode="target")

# some itemData are subsets of larger itemData instances. At the point they will have all been
# marked. Drop any marked targets and modifiers that are a proper subset of another marked
# target or modifier
context.pruneMarks()

# drop any marks that have the CATEGORY "Exclusion"; these are phrases we want to ignore.
context.dropMarks('Exclusion')

# match modifiers to targets
context.applyModifiers()

# Drop any modifiers that didn't get hooked up with a target
context.dropInactiveModifiers()

# put the current markup into an "archive". The archive will later be used to reason across the entire report.


return context
~~~~~~

The markup is stored as a directed graph, so determining whether a target is, for example, negated, you simply check to see if an immediate predecessor of the target node is a negation. This is all done with NetworkX commands.

To access the underlying graph from the context object evoke the getCurrentGraph() method

~~~~
g = context.getCurrentGraph()
~~~~

Here is some code to get a list of all the target nodes in the markup:

~~~~
targets = [n[0] for n in g.nodes(data = True) if n[1].get("category","") == 'target']
~~~~~

Here is a function to test whether a node is modified by any of the categories in a list

~~~~~

def modifies(g,n,modifiers):
"""g: directed graph representing the ConText markup
n: a node in g
modifiers: a list of categories e.g. ["definite_negated_existence","probable_existence"]
modifies() tests whether n is modified by an objects with category in categories"""
pred = g.predecessors(n)
if( not pred ):
return False
pcats = [n.getCategory().lower() for n in pred]
return bool(set(pcats).intersection([m.lower() for m in modifiers]))
~~~~~~