Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

IF 4.7 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of the American Medical Informatics Association Pub Date : 2024-06-20 DOI:10.1093/jamia/ocae092

Sharon Jiang, Barbara D Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R Karger, David Sontag

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引用次数: 0

Abstract

Objective: Leverage 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 methods: We 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.

Results: The 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.

Conclusion: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

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通过机器学习预测肿瘤病历审阅笔记：改善临床医师笔记书写的概念验证策略。

目的：利用电子健康记录（EHR）审计日志开发一种机器学习（ML）模型，该模型可预测临床医生在为肿瘤患者看病时希望查看哪些笔记：利用电子健康记录（EHR）审计日志开发一种机器学习（ML）模型，该模型可预测临床医生在接诊肿瘤患者时希望查看哪些笔记：我们使用笔记元数据和术语频率反向文档频率（TF-IDF）文本表示法训练了逻辑回归模型。我们用精确度、召回率、F1、AUC 和临床定性评估来评价模型的性能：结果：仅元数据模型的 AUC 为 0.930，元数据和 TF-IDF 模型的 AUC 为 0.937。定性评估显示，需要更好的文本表示，并进一步为用户定制预测：我们的模型能有效地显示临床医生在看肿瘤病人时想要查看的前 10 条笔记。进一步的研究可以确定不同类型临床医生用户的特征，并针对不同的医疗环境更好地定制任务：结论：电子病历审计日志可为训练 ML 模型提供重要的相关性数据，而 ML 模型可在肿瘤学环境中协助笔记书写。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Medical Informatics Association 医学-计算机：跨学科应用

CiteScore

14.50

自引率

7.80%

发文量

230

审稿时长

3-8 weeks

期刊介绍： JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.