Enabling Medical Experts to Navigate Clinical Text for Co

Report
Enabling Medical Experts
to navigate clinical text
for cohort identification
(meTAKES)
Stephen Wu, Mayo Clinic
SHARPn Summit 2012
June 12, 2012
Outline
• Motivation
• Methods (current)
•
•
•
•
System architecture
Data retrieval
Search
Cohort management
• Conclusion & Future Work
Motivation
• Clinical NLP out-of-the-box
• Comprehensive knowledge
• Customize? Collaborate!
• Diverse requirements
• Physician/Researcher tasks
• Enroll patients in study
• Define retrospective cohort
• Case abstraction
Somali patients (unique terms)
Drug-induced liver injury (rel’ns)
Pediatric asthma (temporal)
“Medical expert”-driven NLP
• Use case-agnostic
• Use case-specific
• Comprehensive
• Streamlined
• Pre-computed NLP
• On-the-fly NLP
 Diverse requirements  Known requirements
• Interactive interface
• Delivery mechanism
• Available data vs. expert knowledge
source text
semantics
expert criteria
• Web interface (GWT)
Client
Server
data pool parameters
EHR
records
GUI
query
query
parser
NLP
(MedTagger)
cohort mgmt
Lucene
records
cohort
manipulation
ranked records
Data retrieval
• Parameters (current)
• Patient ID
• Date
• Sources (current)
• Enterprise Data Trust (EDT)
•
@ Mayo Clinic
Text files on server
Search
• Parameters (current)
• Term lists
• Logic
• Expansion
• Techniques (current)
• Dictionary (Lucene)
• NLP results
(e.g., negation)
Cohort Management
• Parameters:
• Cohort name/tag
• Selecting patients
• Export
• Iterative refinement
Conclusion and Future Work
• NLP / search
• Text characteristics
• Semantic search
• Relationships
• HCI / cohort
management
• Learning
• Collaboration
• Interoperability
• Structured data
• API
• Mayo delivery:
DDQB Clinical Notes
Search Tool
Evaluation framework
meTAKES team:
Stephen Wu
Ravikumar K.E.
Hongfang Liu
https://sites.google.com/site/stephentzeinnwu
[email protected]
THANK YOU.
Special thanks to:
Siddhartha Jonnalagadda
James Masanz
Vinod Kaggal
Sean Murphy
Tom Suther
Erik Voldal
Carlos Garcia
Melissa Gregg
This work was supported in part by the
SHARPn (Strategic Health IT Advanced
Research Projects) Area 4: Secondary Use of
EHR Data Cooperative Agreement from the
HHS Office of the National Coordinator,
Washington, DC. DHHS 90TR000201.

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