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.
|Original language||English (US)|
|Title of host publication||Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017|
|Editors||Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Dec 15 2017|
|Event||2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States|
Duration: Nov 13 2017 → Nov 16 2017
|Name||Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017|
|Other||2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017|
|Period||11/13/17 → 11/16/17|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT Research reported in this publication was supported by the National Institutes of Health, National Center for Complementary & Integrative Health Award (R01AT009457) (PI: Zhang), the University of Minnesota Clinical and Translational Science Award (8UL1TR000114) (PI: Blazer), and the University of Minnesota Grant-In-Aid award (PI: Zhang). The authors thank Fairview Health Services for data access support of this research.
© 2017 IEEE.
- Clinical Notes
- Electronic Health records
- Machine Learning
- Natural Language Processing