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Medical text extraction

15 Sep 2009. Medical text extraction. Objective. A lot of biomedical articles Too troublesome to read through When all you want to know is: Author, Institution, Research Database, Analysis Tools used, etc. Use information retrieval to extract relevant info from articles. Approach. CRF++

johnavon-gavin
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Medical text extraction

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  1. 15 Sep 2009 Medical text extraction

  2. Objective • A lot of biomedical articles • Too troublesome to read through • When all you want to know is: • Author, Institution, Research Database, Analysis Tools used, etc. • Use information retrieval to extract relevant info from articles

  3. Approach • CRF++ • Training files • XML tagged medical articles • Tagging done by some doctors (from Duke-NUS side)

  4. Tags of importance • Author • Institution • Email • Database Name • Data Analysis Name

  5. Result (1/2) • 3-fold cross-validation • 50 articles used • More available, but not used due to noise (to be cleaned up) • 12 features used

  6. Result (2/2)

  7. Some difficulties • Some peculiar Asian names • Unpredictable for Database name: • <database_name> mfold 3.2 online software </database_name> • <database_name> regional mailing list of the Institute of General Practice, University Hospital Schleswig-Holstein </database_name> • <database_name> hospital and population data set </database_name> • <database_name> national registry </database_name>

  8. The End

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