Heinze et al AMIA 2001

Citation

Daniel T. Heinze et al. 2001. Mining Free-Text Medical Records. In Proceedings of AMIA Symposium, 254-258.

Online version

AMIA Annual Symposium Proceedings Archive

Summary

The paper presents a MEDical Information Extraction (MedIE) system, which extracts patient information from free-text clinical records.

They divided their extraction job into three tasks below.

• extraction of medical terms
• relation extraction
  • extraction of associated medical concepts
  • e.g. Blood pressure & 144/90 in the sentence, "Blood pressure is 144/90"
• text classification
  • e.g. a patient can be classified as a former smoker, a current smoker, or a non-smoker

Their approaches are:

• An ontology-based approach for extracting medical terms of interest
  • they used Unified Medical Language System (UMLS)
  • About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures.
• A graph-based approach which uses the parsing result of link-grammar parser for relation-extraction
  • They included the processing of negation.
  • When the parser fails, they used a pattern-based approach.
  • Because the parser did not process multi-word terms, they replaced the terms with placeholders.
• an NLP-based feature extraction method coupled with an ID3-based decision tree for text classification

This approach was fairly successful mostly showing over 80% of precision and recall. However, the system was tested on the data written by only a clinician, which means that the style of free-text records was consistent.

Related papers

A follow-up paper is Denecke and Bernauer AIME 2007.