PMID:
28824824
DOI:
10.1109/bibm.2016.7822668
Author(s):
Fan, Yadan, He, Lu, Zhang, Rui
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Biochemistry, Genetics and Molecular Biology, Medicine
article description
Clinical notes contain rich information about dietary supplements, which are critical for detecting signals of dietary supplement side effects and interactions between drugs and supplements. One of the important factors of supplement documentation is usage status, such as started and discontinuation. Such information is usually stored in the unstructured clinical notes. We developed a rule-based classifier to identify supplement usage status in clinical notes. The categories referring to the patient's status of supplement use were classified into four classes: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). Clinical notes containing 10 of the most commonly consumed supplements (i.e., alfalfa, echinacea, fish oil, garlic, ginger, ginkgo, ginseng, melatonin, St. John's Wort, and Vitamin E) were retrieved from the University of Minnesota Clinical Data Repository. The gold standard was defined by manually annotating 1000 randomly selected sentences or statements mentioning at least one of these 10 supplements. The rules in the classifier was initially developed on two-thirds of the set of 7 supplements (i.e., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E); the performance was evaluated on the remaining one-third of this set. To evaluate the generalizability of rules, we further validated the second testing set on other 3 supplements (i.e., echinacea, fish oil, and melatonin). The performance of the classifier achieved F-measures of 0.95, 0.97, 0.96, and 0.96 for status C, D, S, and U on 7 supplements, respectively. The classifier also showed good generalizability when it was applied to the other 3 supplements with F-measures of 0.96 for C, 0.96 for D, 0.95 for S, and 0.89 for U. This study demonstrated that the classifier can accurately classify supplement usage status, which can be further integrated as a module into the existing natural language processing pipeline for supporting dietary supplement knowledge discovery.

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