ArticleJMIR medical informatics2021
Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study.
Article in JMIR medical informatics, 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.
- Leveraging Generative Large Language Models for Temporal Relation Extraction From French Clinical Narratives: Prompt-Chaining Approach.Journal of medical Internet research · 2026Article
- A High-Accuracy Rule-Based Algorithm for Automated Extraction of Coronary Artery Calcium Scores from Mixed-Language Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Efficient extraction of medication information from clinical notes: an evaluation in 2 languages.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review.JMIR medical informatics · 2024Article
- Article
- Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Unraveling ethnic disparities in antipsychotic prescribing among patients with psychosis: A retrospective cohort study based on electronic clinical records.Schizophrenia research · 2023Article
- MedLexSp - a medical lexicon for Spanish medical natural language processing.Journal of biomedical semantics · 2023Article
- Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France.Biological psychiatry global open science · 2023Article
- Association between benzodiazepine receptor agonist use and mortality in patients hospitalised for COVID-19: a multicentre observational study.Epidemiology and psychiatric sciences · 2022Observational
- Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment.International journal of environmental research and public health · 2021Article
- Observational Study of Chlorpromazine in Hospitalized Patients with COVID-19.Clinical drug investigation · 2021Observational
- Observational
- Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.Journal of medical Internet research · 2020Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundInformation related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora.
objectiveWe aimed to develop a system to extract medication-related information from clinical text written in French.
methodsWe developed a hybrid system combining an expert rule-based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory-conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches: rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure.
resultsThe overall F-measure was 89.9% (precision 90.8; recall: 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake.
conclusionsAssociating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.
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