ArticleScientific reports2026
A kNN based machine learning approach to automating causality assessment of adverse events.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
8 authors.
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Abstract
This paper introduces a machine learning classifier designed to automate causality assessment in Individual Case Safety Reports (ICSRs), utilizing the principle of event similarity. The classifier’s effectiveness was evaluated using adverse events from six marketed products. Furthermore, we incorporated an augmentation tool to efficiently manage the classification of ‘unassessable’ adverse events. To enhance medical review oversight, we developed a web-based application serving as a reliable decision-support tool for medical reviewers. The outcomes obtained from our model were highly encouraging, emphasizing the potential advantages of utilizing such a model in ICSR causality assessment.
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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.