A Study of Medical Problem Extraction for Better Disease Management.

This study focuses on the extraction of medical problems mentioned in electric health records to support disease management. We experimented with a variety of information extraction methods based on rules, on knowledge bases, and on machine learning, and combined them in an ensemble method approach. A new dataset drawn from cancer patient medical records at the University of Utah Healthcare was manually annotated for all mentions of a selection of the most frequent medical problems in this institution. Our experimental results show that a medical knowledge base can improve shallow and deep learning-based sequence labeling methods. The voting ensemble method combining information extraction models outperformed individual models and yielded more precise extraction of medical problems. As an example of applications benefiting from acurate medical problems extraction, we compared document-level cancer type classifiers and demonstrated that using only medical concepts yielded more accurate classification than using all the words in a clinical note.