ArticleFrontiers in pharmacology2023
Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records.
Article in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- A Large Language Model for Extracting Post-marketing Adverse Drug Events from Clinical Notes in the Electronic Health Record.Drug safety · 2026Article
- The Evaluation of Transformer Models for the Detection of Adverse Drug Events: A Benchmark Study Using Dutch Free-Text Documents of Hospitalized Patients.Drug safety · 2026Article
- Enhanced Adverse-Event Detection and Drug-Event Relation Extraction from Clinical Notes.medRxiv : the preprint server for health sciences · 2026Article
- Graph augmented transformers improve chemotherapy toxicity symptom extraction from clinical notes.Nature communications · 2026Article
- A kNN based machine learning approach to automating causality assessment of adverse events.Scientific reports · 2026Article
- A computationally efficient biomedical text processing framework for pharmacovigilance: integrating low-rank adaptation and interpretable AI for adverse drug reaction detection.Medical & biological engineering & computing · 2026Article
- Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.PloS one · 2026Article
- Leveraging free-text clinical records for heart disease classification through structured feature mapping.PloS one · 2026Article
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Using Natural Language Processing to Identify Symptomatic Adverse Events in Pediatric Oncology: Tutorial for Clinician Researchers.JMIR bioinformatics and biotechnology · 2025Article
- Large language models for structured reporting in radiology: past, present, and future.European radiology · 2025Review
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Role for Artificial Intelligence in the Detection of Immune-Related Adverse Events.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research.Yearbook of medical informatics · 2024Review
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We assessed the generalizability of machine learning methods using natural language processing (NLP) techniques to detect adverse drug events (ADEs) from clinical narratives in electronic medical records (EMRs). We constructed a new corpus correlating drugs with adverse drug events using 1,394 clinical notes of 47 randomly selected patients who received immune checkpoint inhibitors (ICIs) from 2011 to 2018 at The Ohio State University James Cancer Hospital, annotating 189 drug-ADE relations in single sentences within the medical records. We also used data from Harvard's publicly available 2018 National Clinical Challenge (n2c2), which includes 505 discharge summaries with annotations of 1,355 single-sentence drug-ADE relations. We applied classical machine learning (support vector machine (SVM)), deep learning (convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)), and state-of-the-art transformer-based (bidirectional encoder representations from transformers (BERT) and ClinicalBERT) methods trained and tested in the two different corpora and compared performance among them to detect drug-ADE relationships. ClinicalBERT detected drug-ADE relationships better than the other methods when trained using our dataset and tested in n2c2 (ClinicalBERT F-score, 0.78; other methods,
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