ArticleJournal of the American Medical Informatics Association : JAMIA2021
Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 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
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- 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
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- A hybrid system to understand the relations between assessments and plans in progress notes.Journal of biomedical informatics · 2023Article
- CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach.JMIR medical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveWe sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text. MATERIALS AND
methodsTwo tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (bidirectional long short-term memory conditional random field) and BERT (bidirectional encoder representations from transformers) models were trained and compared with CRF model as a baseline to recognize the named entities of DSs and events from clinical notes. In the relation extraction (RE) task, 2 deep learning models, including attention-based Bi-LSTM and convolutional neural network as well as a random forest model were trained to extract the relations between DSs and events, which were categorized into 3 classes: positive (ie, indication), negative (ie, adverse events), and not related. The best performed NER and RE models were further applied on clinical notes mentioning 88 DSs for discovering DSs adverse events and indications, which were compared with a DS knowledge base.
resultsFor the NER task, deep learning models achieved a better performance than CRF, with F1 scores above 0.860. The attention-based Bi-LSTM model performed the best in the RE task, with an F1 score of 0.893. When comparing DS event pairs generated by the deep learning models with the knowledge base for DSs and event, we found both known and unknown pairs.
conclusionsDeep learning models can detect adverse events and indication of DSs in clinical notes, which hold great potential for monitoring the safety of DS use.
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Registered trials
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.