1. Open Health Natural Language Processing Consortium (OHNLP)
Mayo Clinic: Guergana Savova, Ph.D. James Masanz
IBM Watson Research: Anni Coden, Ph.D. Michael Tanenblatt

2. 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…

3. Open Health Natural Language Processing Consortium
(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)

4. 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)

5. Clinical example: clinical Text Analysis and Knowledge Extraction System (cTAKES)
Presenters:
Guergana Savova
James Masanz

6. Overview cTAKES Commitment to both R and D in R&D
Developed at Mayo Clinic
Goals:
Phenotype extraction
Generic – to be used for a variety of retrievals and use 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

7. cTAKES: Components Clinical narrative as a sublanguage Core components
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)

8. Output Example: Disorder Object
“No evidence of unstable angina.”
Disorder
Text: unstable angina
Associated code: SNOMED
Named entity type: disease/disorder
Status: current
Negation: true

9. Methods Preliminary results:
Savova, Guergana; Kipper-Schuler, Karin; Buntrock, James and Chute, Christopher 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)

10. cTAKES demo

11. Medical example: Medical Knowledge Analysis System MedKAT and MedKAT/P
Presenters:
Anni Coden
Michael Tanenblatt

12. Overview MedKAT and MedKAT/P
Developed at IBM
Goal:
Identification of concepts and their attributes based on 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

13. Core Components Document structure
Syntactic tools (tokenization ... Shallow parsing)
Concept identification
Negation
Relationship extraction
Extracted data
F-score
Anatomic site
0.95
Histology
0.98
Size
1.00
Date
Grade
Gross Desc
0.80
Lymph Nodes
0.81
Primary Tumor
0.82
Metastatic Tumor
0.65

14. Document Structure 16

15. Document Structure 17

16. Document Structure 18

17. Output

18. Cancer Disease Knowledge Representation Model

19. Demos Query by Model / Cancer
Detailed view of annotations in Document Analyzer
m/research_projects.nsf/pages/medic alinformatics.index.html

20. Adaptation
Presenters:
Anni Coden
Michael Tanenblatt

21. Adaptation Sentence breaks Text case Part of speech tags
Shallow parser
Dictionary lookup
Document structure

22. Sentence Breaks

23. Sentence Breaks Some solutions: Use annotator to re-break sentences
Retrain tagger

24. Case/Part of Speech Tags

25. Case/Part of Speech Tags
Some solutions:
Retrain tagger
Use UIMA annotator to create a “true case” view

26. Part of Speech Tags

27. Part of Speech Tags Some solutions: Retrain tagger
Use dictionary lookup to modify incorrect tags
Create rule-based annotator to modify incorrect tags

28. Shallow Parser

29. Shallow Parser 31

30. Shallow Parser 32

31. 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

32. 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)

33. Discussion: Future of OHNLP.ORG
Provided seed annotators and tools
Goal: growing community
Annotators, tools
Methodologies
Gold standards
Common type system for plug-and- play
What are the hurdles?

34. Hands-on Customization

35. MedKAT Dictionary adaptation Concept identification parameters
Document structure detection

36. cTAKES
Negation window
Lookup window
Dictionary modifications

37. Questions?