Evaluating Automatic Methods to Extract Patients' Supplement Use from Clinical Reports.

Abstract

The widespread prevalence of dietary supplements has drawn extensive attention due to the safety and efficacy issue. Clinical notes document a great amount of detailed information on dietary supplement usage, thus providing a rich source for clinical research on supplement safety surveillance. Identification the use status of dietary supplements is one of the initial steps for the ultimate goal of the supplement safety surveillance. In this study, we built rule-based and machine learning-based classifiers to automatically classify the use status of supplements into four categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). In comparison to the machine learning classifier trained on the same datasets, the rule-based classifier showed a better performance with F-measure in the C, D, S, U status of 0.93, 0.98, 0.95, and 0.83, respectively. We further analyzed the errors generated by the rule-based classifier. The classifier can be potentially applied to extract supplement information from clinical notes for supporting research and clinical practice related to patient safety on supplement usage.