Natural Language Processing (NLP) for Clinical Decision Support: A Practical Approach

A significant portion of the electronic documentation of clinical care is captured in the form of unstructured narrative text like psychotherapy and progress notes. Despite the big push to adopt structured data entry (as required by the Meaningful Use incentive program for example), many clinicians still like to document care using free narrative text. The advantage of using narrative text as opposed to coded entries is that narrative text can tell the story of the patient and the care provided particularly in complex cases. My opinion is that free narrative text should be used to complement coded entries when necessary to capture relevant information.

Furthermore, medical knowledge is expanding very rapidly. For example, PubMed has more than 24 millions citations for biomedical literature from MEDLINE, life science journals, and online books. It is impossible for the human brain to keep up with that amount of knowledge. These unstructured sources of knowledge contain the scientific evidence that is required for effective clinical decision making in what is referred to as Evidence-Based Medicine (EBM).

In this blog, I discuss two practical applications of Natural Language Processing (NLP). The first is the use of NLP tools and techniques to automatically extract clinical concepts and other insight from clinical notes for the purpose of providing treatment recommendations in Clinical Decision Support (CDS) systems. The second is the use of text analytics techniques like clustering and summarization for Clinical Question Answering (CQA).

The emphasis of this post is on a practical approach using freely available and mature open source tools as opposed to an academic or theoretical approach. For a theoretical treatment of the subject, please refer to the book Speech and Language Processing by Daniel Jurafsky and James Martin.

Clinical NLP with Apache cTAKES

Based on the Apache Unstructured Information Management Architecture (UIMA) framework and the Apache OpenNLP natural language processing toolkit, Apache cTAKES provides a modular architecture utilizing both rule-based and machine learning techniques for information extraction from clinical notes. cTAKES can extract named entities (clinical concepts) from clinical notes in plain text or HL7 CDA format and map these entities to various dictionaries including the following Unified Medical Language System (UMLS) semantic types: diseases/disorders, signs/symptoms, anatomical sites, procedures, and medications.

cTAKES includes the following key components which can be assembled to create processing pipelines:

• Sentence boundary detector based on the OpenNLP Maximum Entropy (ME) sentence detector.
• Tokenizor
• Normalizer using the National Library of Medicine's Lexical Variant Generation (LVG) tool
• Part-of-speech (POS) tagger
• Shallow parser
• Named Entity Recognition (NER) annotator using dictionary look-up to UMLS concepts and semantic types. The Drug NER can extract drug entities and their attributes such as dosage, strength, route, etc.
• Assertion module which determines the subject of the statement (e.g., is the subject of the statement the patient or a parent of the patient) and whether a named entity or event is negated (e.g., does the presence of the word "depression" in the text implies that the patient has depression).

Apache cTAKES 3.2 has added YTEX, a set of extensions developed at Yale University which provide integration with MetaMap, semantic similarity, export to Machine Learning packages like Weka and R, and feature engineering.

The following diagram from the Apache cTAKES Wiki provides an overview of these components and their dependencies (click to enlarge):

Source: Apache cTAKES wiki

Massively Parallel Clinical Text Analytics in the Cloud with GATECloud

The General Architecture for Text Engineering (GATE) is a mature, comprehensive, and open source text analytics platform. GATE is a family of tools which includes:

• GATE Developer: an integrated development environment (IDE) for language processing components with a comprehensive set of available plugins called CREOLE (Collection of REusable Objects for Language Engineering). 
• GATE Embedded: an object library for embedding services developed with GATE Developer into third-party applications.
• GATE Teamware: a collaborative semantic annotation environment based on a workflow engine for creating manually annotated corpora for applying machine learning algorithms. 
• GATE Mímir: the "Multi-paradigm Information Management Index and Repository" which supports a multi-paradigm approach to index and search over text, ontologies, and semantic metadata.
• GATE Cloud: a massively parallel clinical text analytics platform (Platform as a Service or PaaS) built on the Amazon AWS Cloud.

What makes GATE particularly attractive is the recent addition of GATECloud.net PaaS which can boost the productivity of people involved in large scale text analytics tasks.

 

Clustering, Classification, Text Summarization, and Clinical Question Answering (CQA)

 

An unsupervised machine learning approach called Clustering can be used to classify large volumes of medical literature into groups (clusters) based on some similarity measure (such as the Euclidean distance). Clustering can be applied at the document, search result, and word/topic levels. Carrot2 and Apache Mahout are open source projects that provide several methods for document clustering. For example, the Latent Dirichlet Allocation learning algorithm in Apache Mahout automatically clusters words into topics and documents into mixtures of topics. Other clustering algorithms in Apache Mahout include: Canopy, Mean-Shift, Spectral, K-Means and Fuzzy K-Means. Apache Mahout is part of the Hadoop ecosystem and can therefore scale to very large volumes of unstructured text.

Document classification essentially consists in assigning predefined set of labels to documents. This can be achieved through supervised machine learning algorithms. Apache Mahout implements the Naive Bayes classifier.

Text summarization techniques can be used to present succinct and clinically relevant evidence to clinicians at the point of care. MEAD (http://www.summarization.com/mead/) is an open source project that implements multiple summarization algorithms. In the biomedical domain, SemRep is a program that extracts semantic predications (subject-relation-object triples) from biomedical free text. Subject and object arguments of each predication are concepts from the UMLS Metathesaurus and the relation is from the UMLS Semantic Network (e.g., TREATS, Co-OCCURS_WITH). The SemRep summarization provides a short summary of these concepts and their semantic relations.

AskHermes (Help clinicians to Extract and aRrticulate Multimedia information for answering clinical quEstionS) is a project that attempts to implement these techniques in the clinical domain. It allows clinicians to enter questions in natural language and uses the following unstructured information sources: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines, and Wikipedia articles.

The processing pipeline in AskHermes includes the following: Question Analysis, Related Questions Extraction, Information Retrieval, Summarization and Answer Presentation. AskHermes performs question classification using MMTx (MetaMap Technology Transfer) to map keywords to UMLS concepts and semantic types. Classification is achieved through supervised machine learning algorithms such as Support Vector Machine (SVM) and conditional random fields (CFRs). Summarization and answer presentation are based on clustering techniques. AskHermes is powered by open source components including: JBoss Seam, Weka, Mallet , Carrot2 , Lucene/Solr, and WordNet (a lexical database for the English language).

Visual Analytics for Clinical Decision Making

In my last post, I talked about the era of Big Data in medicine, Evidence-Based Practice (EBP),  Practice-Based Evidence (PBE), and the need for a human-centered approach to building intelligent health IT (iHIT) systems. In this post, I discuss Visual Analytics, an emerging discipline in Data Science. In a report titled "Illuminating the Path: The R&D Agenda for Visual Analytics" published in 2004 by the National Visualization and Analytics Center (NVAC), Visual Analytics is defined as "the science of analytical reasoning facilitated by visual interactive interfaces."

The goal of Visual Analytics is to obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. As a multidisciplinary field, Visual Analytics combines several disciplines such as human perception and cognition, interactive graphic design, statistical computing, data mining, spatio-temporal data analysis, and even art.

In his book titled "Beautiful Evidence", Edward Tufte illustrates the fundamental principles of analytical design by using Charles Minard's famous map known as "Carte figurative des pertes successives en hommes de l'Armée Française dans la campagne de Russie 1812-1813" (Figurative Map of the successive losses in men of the French Army in the Russian Campaign 1812-1813). The map is a dramatic account of the heavy losses of the french army during Napoleon's Russian campaign of 1812. Edward Tuffe calls the map the "best statistical graphics ever". Click on the image below to enlarge it.

Visual Analytics is also an emerging discipline in healthcare informatics. For example, similar to Minard's map of the Russian Campaign of 1812-1813, Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes over a certain period of time through the vivid showing of causes, variables, comparisons, and explanations. This approach contrasts with the traditional display of clinical data in table rows that is so common in electronic health record (EHR) systems interfaces.

Another Visual Analytics technique called Visual Cluster Analysis can be particularly helpful in Comparative Effectiveness in clinical care settings where the goal is to compare the benefits and harms of different interventions for different subgroups (groups of patients sharing similar clinical characteristics such as age, gender, race, genetic profile, and comorbidities). Given a specific patient, Visual Cluster Analysis can help the clinician visually explore what works and what doesn't work for "similar patients".

You can find interesting examples of research projects and implementations in the proceedings of the Visual Analytics in Healthcare Workshop which has been held in conjunction with the IEEE VisWeek for the past three years. The 2013 Visual Analytics in Healthcare Summit (VHAC 2013) will be held in conjunction with the AMIA 2013 conference in Washington DC. There are a number of open source toolkits that can be used to implement Visual Analytics. Some of them are based on open web standards such as HTML5, CSS3, SVG, and Javascript. My favorite is D3.js. DC.js and Crossfilter are built on top of D3.js and facilitate the creation of interactive visualization of multivariate datasets in the browser.