ArticleDrug safety2019
Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.
Article in Drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.Pharmaceutical medicine · 2022Pooled it
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Omics-based Cancer Prognosis Across Ethnic Groups: From Feature Engineering to Disparity Detection and Mitigation.IEEE transactions on artificial intelligence · 2026Article
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Article
- Lightweight transformers for clinical natural language processing.Natural language engineering · 2024Article
- Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.Pragmatic and observational research · 2024Article
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- Classifying Drug Ratings Using User Reviews with Transformer-Based Language Models.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set.JMIR medical informatics · 2022Article
- A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media.IEEE journal of biomedical and health informatics · 2022Article
- Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method.JMIRx med · 2021Article
- Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data.JAMIA open · 2020Article
- Medical Information Extraction in the Age of Deep Learning.Yearbook of medical informatics · 2020Review
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.JMIR medical informatics · 2020Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and significanceAdverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. Using state-of-the-art deep-learning neural networks to jointly model concept and relation extraction, we achieved the highest integrated task score in the 2018 Medication and Adverse Drug Event (MADE) 1.0 challenge.
methodsWe used a combined bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) neural network to detect medical entities relevant to ADEs and a combined BiLSTM and attention network to determine relations, including the adverse drug reaction relation between medication and sign or symptom entities. Using these models, we conducted three experiments: (1) separate and sequential modeling of entities and relations; (2) joint modeling where relations between medications and sign or symptoms determined ADE and indication entities; (3) use of information from external resources such as the US FDA's adverse event database as additional input to the second method.
resultsJoint modeling improved the overall task accuracy from 0.62 to 0.65 F measure, and the additional use of external resources improved the accuracy to 0.66 F measure. Given the gold-standard medical entity labels, the joint model plus external resources method achieved F measures of 0.83 for ADE-relevant medical entity detection and 0.87 for relation detection.
conclusionIt is important to use joint modeling techniques and external resources for effectively detecting ADEs from clinical narratives in electronic health record (EHR) systems. While the extraction of entities and relations individually achieved high accuracy, the integrated task still has room for further improvement.
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30649738What OpenQuestion holds
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.