Open Health Natural Language
Processing Consortium
(OHNLP)
Mayo Clinic:
Guergana Savova, Ph.D.
James Masanz
[email protected]
IBM Watson Research:
Anni Coden, Ph.D.
Michael Tanenblatt
[email protected]
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Overview
• OHNLP? Oh, NLP?
• Demo of a clinical OHNLP system
(cTAKES)
• Demo of a medical OHNLP system
(MedKAT) with extensions to pathology
(/P)
• How can I adapt the system to my data?
• Lively discussion: how can I get involved,
OHNLP future steps…
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Open Health Natural Language
Processing Consortium
• www.ohnlp.org (part of caBIG Vocabulary
Knowledge Center web presence)
• Goal
• Foster an open-source collaborative community around
clinical NLP that can deliver best-of-breed annotators,
leverage the dynamic features of UIMA flow-control,
and establish the infrastructure for clinical NLP.
• Two open source releases as part of OHNLP
• Mayo’s pipeline for processing clinical notes (cTAKES)
• IBM’s pipeline for processing medical notes (MedKAT)
and pathology reports (MedKAT/P)
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Other non-OHNLP clinical NLP
Systems
• Proprietary
• medLEE (Columbia University)
• Topaz (University of Pittsburgh)
• Vanderbilt University
• caTIES (University of Pittsburgh)
• MPLUS/Onyx (University of Utah)
• VA Hospital system
• Open Source
• i2b2 HITEx (Health Information Text Extraction)
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Clinical example:
clinical Text Analysis and
Knowledge Extraction System
(cTAKES)
Presenters:
Guergana Savova
James Masanz
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Overview
• cTAKES
• Developed at Mayo Clinic
• Goals:
• Phenotype extraction
• Generic – to be used for a variety of retrievals and use
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•
cases
Expandable – at the information model level and
methods
Modular
Cutting edge technologies – best methods combining
existing practices and novel research with rapid
technology transfer
Best software practices (80M+ notes)
• Commitment to both R and D in R&D
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cTAKES: Components
• Clinical narrative as a sublanguage
• Core components
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Sentence boundary detection (OpenNLP technology)
Tokenization (rule-based)
Morphologic normalization (NLM’s LVG)
POS tagging (OpenNLP technology)
Shallow parsing (OpenNLP technology)
Named Entity Recognition
• Dictionary mapping (lookup algorithm)
• Machine learning (MAWUI)
• Negation and context identification (NegEx)
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Output Example: Disorder Object
• “No evidence of unstable angina.”
• Disorder
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Text: unstable angina
Associated code: SNOMED 4557003
Named entity type: disease/disorder
Status: current
Negation: true
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Methods
• Preliminary results:
• Savova, Guergana; Kipper-Schuler, Karin;
Buntrock, James and Chute, Christopher. 2008.
UIMA-based clinical information extraction
system. LREC 2008: Towards enhanced
interoperability for large HLT systems: UIMA for
NLP.
• Manuscript with detailed system description and
evaluation under review (JAMIA)
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cTAKES demo
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Medical example:
Medical Knowledge Analysis System
MedKAT and MedKAT/P
Presenters:
Anni Coden
Michael Tanenblatt
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Overview
• MedKAT and MedKAT/P
• Developed at IBM
• Goal:
• Identification of concepts and their attributes based on
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•
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a standard or proprietary terminology/ontology
/P adaptation to pathology reports – relation extraction
Modular, Generic, Expandable
• Terminology, Conceptual Model
Easy adaptation to specific corpus and conventions
Integration into institutional system
• Ongoing commitment to Research and Development
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Core Components
• Document structure
• Syntactic tools
(tokenization ...
Shallow parsing)
• Concept
identification
• Negation
• Relationship
extraction
Extracted data
Anatomic site
Histology
Size
Date
Grade
Gross Desc
Lymph Nodes
Primary Tumor
Metastatic Tumor
F-score
0.95
0.98
1.00
1.00
0.98
0.80
0.81
0.82
0.65
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Document Structure
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Document Structure
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Document Structure
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Output
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Cancer Disease Knowledge
Representation Model
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Demos
• Query by Model / Cancer
• Detailed view of annotations in
Document Analyzer
• http://domino.research.ibm.com/com
m/research_projects.nsf/pages/medic
alinformatics.index.html
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Adaptation
Presenters:
Anni Coden
Michael Tanenblatt
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Adaptation
• Sentence breaks
• Text case
• Part of speech tags
• Shallow parser
• Dictionary lookup
• Document structure
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Sentence Breaks
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Sentence Breaks
• Some solutions:
• Use annotator to re-break sentences
• Retrain tagger
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Case/Part of Speech Tags
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Case/Part of Speech Tags
• Some solutions:
• Retrain tagger
• Use UIMA annotator to create a “true
case” view
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Part of Speech Tags
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Part of Speech Tags
• Some solutions:
• Retrain tagger
• Use dictionary lookup to modify
incorrect tags
• Create rule-based annotator to
modify incorrect tags
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Shallow Parser
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Shallow Parser
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Shallow Parser
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Dictionary Lookup
• Dictionary entries can be added,
changed, deleted
• Dictionary entry attributes can be
added, changed, deleted
• Search parameters can be modified
• Post processing filters
• Tokenization of text and dictionary
should be the same
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Document Structure
• Plain text or XML (e.g., CDA)
• Processes specific document section
types (e.g., diagnosis)
• Detection of formatting (e.g. bullets)
• Detection of relations between
sections
• Making implicit conventions explicit
(e.g. meaning of title)
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Discussion: Future of OHNLP.ORG
• Provided seed annotators and tools
• Goal: growing community
• Annotators, tools
• Methodologies
• Gold standards
• Common type system for plug-andplay
• What are the hurdles?
Hands-on Customization
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MedKAT
• Dictionary adaptation
• Concept identification parameters
• Document structure detection
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cTAKES
• Negation window
• Lookup window
• Dictionary modifications
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Questions?
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