机器学习模型使用电子健康记录检测和预测患者安全事件:系统性审查
Ghasem Deimazar1, Abbas Sheikhtaheri1
1Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
International journal of medical informatics
|October 14, 2023
概括
机器学习有效地识别电子健康记录 (EHR) 中的患者安全事件. 然而,大多数算法需要进一步验证和前性测试,以便在临床环境中提高性能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床数据分析 临床数据分析
背景情况:
- 电子健康记录 (EHR) 为识别患者安全事件提供了丰富的数据来源.
- 自动机器学习 (ML) 方法可以通过检测这些事件来提高医疗保健的效率和质量.
- 患者安全事件管理对于改善医疗保健服务提供至关重要.
研究的目的:
- 系统地审查基于ML的方法和技术,以使用EHR来管理患者安全事件.
- 分析各种ML算法的性能和结果,以检测和预测患者安全事件.
- 确定最有效的ML方法来管理EHR数据中的患者安全事件.
主要方法:
- 在主要的科学数据库 (Scopus,PubMed,科学网,EMBASE,IEEE Xplore) 进行了全面的文献搜索.
- 审查了专注于ML技术的研究,用于自动预测和检测患者安全事件和电子健康记录中的医疗错误.
- 选和分析了41篇相关论文的方法和结果.
主要成果:
- 支持向量机 (SVM),随机森林,条件随机场 (CRF) 和双向长期短期记忆与条件随机场 (BiLSTM-CRF) 是常用的算法.
- 在提取诸如药物不良事件 (ADEs) 和药物不良反应 (ADRs) 等概念方面,BiLSTM-CRF表现出色.
- 循环神经网络 (RNN) 和BiLSTM-CRF在ADE检测方面表现优异,而线性分类器和天真贝叶斯 (NB) 在ADR检测方面是有效的. 后勤回归在手术部位感染方面表现最好.
结论:
- 机器学习在自动检测和预测患者安全事件方面具有重大潜力.
- 当前的ML算法往往缺乏外部验证和前性测试,限制了它们的即时临床适用性.
- 进一步的研究对于提高自动化患者安全事件检测系统的性能和可靠性至关重要.
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