ArticleBMC medical informatics and decision making2018
Using natural language processing methods to classify use status of dietary supplements in clinical notes.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- EPEE: towards efficient and effective foundation models in biomedicine.npj health systems · 2026Article
- MMRAG: multi-mode retrieval-augmented generation with large language models for biomedical in-context learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.Journal of medical Internet research · 2025Article
- RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes.Journal of biomedical informatics · 2023Review
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Extracting medication changes in clinical narratives using pre-trained language models.Journal of biomedical informatics · 2023Article
- Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- iDISK: the integrated DIetary Supplements Knowledge base.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017).BMC medical informatics and decision making · 2018Article
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2 authors.
Funding
Abstract
backgroundDespite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records (EHRs) provide a potential way for active pharmacovigilance on dietary supplements since a fair amount of dietary supplement information, especially those on use status, can be found in clinical notes. Extracting such information is extremely significant for subsequent supplement safety research.
methodsIn this study, we collected 2500 sentences for 25 commonly used dietary supplements and annotated into four classes: Continuing (C), Discontinued (D), Started (S) and Unclassified (U). Both rule-based and machine learning-based classifiers were developed on the same training set and evaluated using the hold-out test set. The performances of the two classifiers were also compared.
resultsThe rule-based classifier achieved F-measure of 0.90, 0.85, 0.90, and 0.86 in C, D, S, and U status, respectively. The optimal machine learning-based classifier (Maximum Entropy) achieved F-measure of 0.90, 0.92, 0.91 and 0.88 in C, D, S, and U status, respectively. The comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles.
conclusionsMachine learning-based classifier outperforms rule-based classifier in categorization of the use status of dietary supplements in clinical notes. Future work includes applying deep learning methods and developing a hybrid system to approach use status classification task.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.