Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC).
Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application.
D'Avolio
Leonard W
LW
Massachusetts Veterans Epidemiology Research and Information Center Cooperative Studies Coordinating Center, VA Boston Healthcare System, Jamaica Plain, Massachusetts 02130, USA. leonard.davolio@va.gov
Nguyen
Thien M
TM
Farwell
Wildon R
WR
Chen
Yongming
Y
Fitzmeyer
Felicia
F
Harris
Owen M
OM
Fiore
Louis D
LD
D000465:Algorithms; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D038801:International Classification of Diseases; D009323:Natural Language Processing; D009369:Neoplasms / Q000145:classification / Q000473:pathology; D012986:Software Validation; D014584:User-Computer Interface