ArticleClinical pharmacology and therapeutics2021
Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.
Article in Clinical pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Prompt Engineering of Large Language Models for Medication Dose Calculation.Clinical pharmacology and therapeutics · 2026Article
- Identification of antibiotic allergies in children using prescribing patterns in the electronic medical record.British journal of clinical pharmacology · 2026Article
- Classification of health product defect reports by deep learning.Scientific reports · 2026Article
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A national pharmacovigilance centre perspective on pandemic preparedness -Frontiers in drug safety and regulation · 2025Article
- SMS-Based Active Surveillance of Adverse Events following Immunization in Children: The VigiVax Study.Vaccines · 2024Article
- Article
- Disponibilité de l'information médicale requise pour la déclaration d'une réaction indésirable médicamenteuse à Santé Canada: une étude exploratoire.The Canadian journal of hospital pharmacy · 2024Article
- BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance.PLOS digital health · 2023Article
- Natural language processing for automated triage and prioritization of individual case safety reports for case-by-case assessment.Frontiers in drug safety and regulation · 2023Article
- Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.Drug safety · 2022Article
- Intelligent Telehealth in Pharmacovigilance: A Future Perspective.Drug safety · 2022Review
- Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.Drug safety · 2022Article
- Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.Drug safety · 2022Article
- Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer.Cancer cell international · 2022Article
- Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.Frontiers in endocrinology · 2022Article
- Detection of Drug-Induced Thrombocytopenia Signals in Children Using Routine Electronic Medical Records.Frontiers in pharmacology · 2021Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised machine learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 and March 2019 (n = 2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n = 187). Here, we show that regarding area under the curve (AUC) and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 (0.92-0.94) and F-measure of 0.72 (0.68-0.75). This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information.
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