Text Mining of Medical Documents

Michael Elhadad - Raphael Cohen

Dept of Computer Science

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

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”

Specific Interest

• EHR– 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.

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, pp121-128

• 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