1. Text Mining of Medical Documents
Michael Elhadad - Raphael Cohen
Dept of Computer Science

2. Natural Language Processing
Analyze free text to extract “information”
Key challenges:
Ambiguity: heart, ברק
Variability: diabetes, dm, diab.
Applications:
Search
Text Mining: information extraction, relations
Summarization

3. NLP for Medical Domain Opportunity
Availability of online textual documents
EHR: mostly textual (release notes)
Scientific literature (PubMed)
Challenge
Methods developed on “regular language” fail on “medical language”

4. Specific Interest EHR Hebrew NLP Domain Adaptation
Exploit rich textual data in EHR.
In Hebrew!
Hebrew NLP
Complex morphology, no dictionaries, no UMLS
Domain Adaptation
Machine learning methods to port NLP models from one domain to medical domain.

5. Recent Work in Domain
Raphael Cohen, Michael Elhadad and Ohad S Birk, Analysis of free online physician advice services, PLOS ONE, 2013
Raphael Cohen, Noemie Elhadad, Michael Elhadad, Redundancy in Electronic Health Record Corpora: Analysis, Impact on Text Mining Performance and Mitigation Strategies BMC Bioinformatics, 2013.
Raphael Cohen and Michael Elhadad, Syntactic Dependency Parsers for Biomedical-NLP, AMIA Proceedings 2012, pp
Raphael Cohen, Yoav Goldberg and Michael Elhadad, Domain Adaptation of a Dependency Parser with a Class-Class Selectional Preference Model, ACL 2012, SRW
Raphael Cohen, Avitan Gefen, Michael Elhadad and Ohad S Birk, CSI-OMIM - Clinical Synopsis Search in OMIM, BMC Bioinformatics 2011, 12:65