ArticleJournal of the American Medical Informatics Association : JAMIA2024
Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Comments on Contemporary Uses of Machine Learning for Electronic Health Records.North Carolina medical journal · 2024Review
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Authors and funding
8 authors.
Funding
Abstract
objectiveLeverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND
methodsWe trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.
resultsThe metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. DISCUSSION: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.
conclusionEHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.
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Registered trials
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