预测患者报告结果措施:对人工智能引导的患者偏好预测器进行范围审查
Jeremy A Balch1,2, A Hayes Chatham2, Philip K W Hong1
1Department of Surgery, University of Florida, Gainesville, FL, United States.
Frontiers in artificial intelligence
|November 20, 2024
概括
使用机器学习对有能力的患者预测患者报告结果 (PROM) 对有能力的个体开发患者偏好预测器 (PPP) 显示出希望. 需要进一步的研究来改善模型性能和公平性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 患者报告的结果
背景情况:
- 患者偏好预测器 (PPP) 旨在协助那些缺乏预先指令的失能患者的决策.
- 构建个性化的PPP面临实际障碍,需要探索机器学习应用程序.
- 关于预测患者报告结果措施 (PROMs) 对于有能力的患者的先前研究提供了洞察力.
研究的目的:
- 审查机器学习应用在预测有能力的患者的PROM.
- 确定为残疾患者开发个性化的PPP的机会.
- 评估目前对PROM预测机器学习研究的现状.
主要方法:
- 使用PRISMA-ScR指南进行了范围审查.
- 搜索的数据库包括 PubMed,Embase 和 Scopus.
- 包括专注于用于PROM预测和理论PPP的机器学习的研究.
主要成果:
- 68项研究利用机器学习进行PROM预测,20项研究探讨了理论PPP.
- 骨科和脊椎手术是PROM预测的常见背景.
- 预测因素包括人口统计,事件前的PROM,并发症,社会决定因素和手术内变量.
- 模型性能一般为低至中等,事件前PROM最具预测性.
- 公平性评估很少发生.
结论:
- 在有能力的患者中预测PROM的机器学习为为有能力的患者开发PPP带来了挑战和机会.
- 除了人口统计之外,整合患者价值观对于有效的PPP开发至关重要.
- 改善模型性能和公平性评估对于临床实用性是必要的.
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