机器学习方法用于预测药物不服药:一个范围审查
Christian Rhudy1, Jacob Johnson1, Courtney Perry2
1University of Kentucky Healthcare, Pharmacy Services, Lexington, KY, USA.
预测模型可以识别患有药物不服药风险的患者. 使用诊断或患者报告数据的机器学习模型显示,积极干预有望改善临床结果.
科学领域:
- 药物监督和健康信息学
- 医疗保健中的机器学习
- 临床决策支持系统 临床决策支持系统
背景情况:
- 药物不服药是不良临床结果的重要,可预防的原因.
- 预测建模为主动干预提供了一条途径,以减轻不遵守风险.
研究的目的:
- 进行对预测药物坚持的机器学习模型的范围审查.
- 识别关键预测因素,模型培训/评估方法和遵守分类策略.
- 为开发临床可操作的粘附预测模型提供信息.
主要方法:
- 系统的文献搜索 (PubMed,Embase,Web of Science) 关于机器学习用于药物坚持预测的研究 (2015-2024年).
- 不包括会议摘要,综述,议定书和不可访问的完整文本.
- 基于接收器运行特征曲线 (AUC) 下面面积的模型的定量分析,重点关注每项研究中表现最高的模型.
主要成果:
- 包括52项研究;14项具有偏差低风险. 使用诊断 (AUC 0.837) 或受试者报告的数据 (AUC 0.828) 的初级模型显示出更高的预测性能.
- 关键预测因素包括对药物的信念,并发症,药物历史,先前的坚持和社会经济因素.
- 随机森林和物流回归通常是表现最好的模型.
结论:
- 在坚持预测建模和评估中存在显著的变化.
- 确定了成功的算法,预测器和训练技术.
- 未来的研究必须优先考虑有效的临床决策支持工具的操作可行性和临床实用性.
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