Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)

Abstract

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.

Figure 1

Current processes of clinical information retrieval (IR) versus those proposed in the design of the automated retrieval console (ARC).

Figure 2

Automated retrieval console (ARC) menu, showing the various ARC interfaces.

Figure 3

A screen shot of the Judge interface. The annotation instructions shown in the ‘Help Information’ window is populated as part of the creation of a new project.

Figure 4

A graphical representation of the study design. CRC, colorectal cancer; CRF, conditional random field; MaxEnt, maximum entropy; NLP, natural language processing.