Metadata-Version: 1.1
Name: pyConTextNLP
Version: 0.6.1.0
Summary: A Python implementation of the ConText algorithm
Home-page: https://github.com/chapmanbe/pyConTextNLP
Author: Brian Chapman
Author-email: brian.chapman@utah.edu
License: http://www.apache.org/licenses/LICENSE-2.0
Description: 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
        -  Danielle Mowery
        
        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 code has been modified substantially since the code base used for
        the JBI publication. In the current version, pyConTextNLP corresponds to
        pyConTextGraph in previous versions. This code uses
        [http://networkx.lanl.gov/ NetworkX] to structure the relationship
        between targets and modifiers in the markup.
        
        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.pyConText.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. The
        ``regular expression`` (optional) used to capture the literal in the
        text. If no regular expression is provided, a regular expression is
        generated literally from the literal. 4. The ``rule`` (optional). 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]))
        
Keywords: ConText NLP
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
