ArticleJMIR medical informatics2020
Identification of Adverse Drug Event-Related Japanese Articles: Natural Language Processing Analysis.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
- Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study.Journal of medical Internet research · 2026Article
- A Novel QR Code-Based Solution for Secure Electronic Health Record Transfer in Venous Thromboembolism Home Rehabilitation Management: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2025Article
- Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study.JMIR medical informatics · 2025Article
- Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases.Scientific reports · 2024Article
- Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.PloS one · 2024Article
- An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records.Methods of information in medicine · 2023Article
- Development of a novel drug information provision system for Kampo medicine using natural language processing technology.BMC medical informatics and decision making · 2023Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Using Natural Language Processing Techniques to Detect Adverse Events From Progress Notes Due to Chemotherapy.Cancer informatics · 2022Article
- Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study.JMIR medical informatics · 2021Article
- Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing.Yearbook of medical informatics · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical articles covering adverse drug events (ADEs) are systematically reported by pharmaceutical companies for drug safety information purposes. Although policies governing reporting to regulatory bodies vary among countries and regions, all medical article reporting may be categorized as precision or recall based. Recall-based reporting, which is implemented in Japan, requires the reporting of any possible ADE. Therefore, recall-based reporting can introduce numerous false negatives or substantial amounts of noise, a problem that is difficult to address using limited manual labor.
objectiveOur aim was to develop an automated system that could identify ADE-related medical articles, support recall-based reporting, and alleviate manual labor in Japanese pharmaceutical companies.
methodsUsing medical articles as input, our system based on natural language processing applies document-level classification to extract articles containing ADEs (replacing manual labor in the first screening) and sentence-level classification to extract sentences within those articles that imply ADEs (thus supporting experts in the second screening). We used 509 Japanese medical articles annotated by a medical engineer to evaluate the performance of the proposed system.
resultsDocument-level classification yielded an F1 of 0.903. Sentence-level classification yielded an F1 of 0.413. These were averages of fivefold cross-validations.
conclusionsA simple automated system may alleviate the manual labor involved in screening drug safety-related medical articles in pharmaceutical companies. After improving the accuracy of the sentence-level classification by considering a wider context, we intend to apply this system toward real-world postmarketing surveillance.
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