基于机器学习的复杂慢性病患者临床状况变化的预测:两期试点前性单中心观察性研究
Celia Alvarez-Romero1, Alejandro Polo-Molina2, Eugenio Francisco Sánchez-Úbeda2
1Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of, Seville, Spain.
JMIR formative research
|April 19, 2024
概括
可穿戴活动追踪器可以预测复杂慢性疾病患者的功能障碍. 这种人工智能模型使用移动数据来评估巴特尔指数 (BI),帮助疾病管理.
科学领域:
- 老年学和康复医学 老年学和康复医学
- 医疗保健中的人工智能
- 数字健康和可穿戴技术
背景情况:
- 功能障碍是复杂慢性疾病的关键预后指标,标志着疾病进展.
- 功能衰退的早期识别需要及时的诊断和治疗干预.
- 预测临床和功能状态对于管理慢性疾病至关重要.
研究的目的:
- 在患有复杂慢性疾病的患者中预测巴特尔指数 (BI).
- 用人工智能模型评估临床和功能状态.
- 为了利用物联网 (IoT) 移动设备进行数据收集.
主要方法:
- 一个两阶段的前性观察试点研究.
- 招募患有复杂慢性疾病的患者和分配可穿戴活动追踪器.
- 使用机器学习 (决策树) 和统计方法 (SPSS,Python) 分析移动数据.
主要成果:
- 人工智能模型准确地预测了巴特尔指数 (BI) 类,准确度为88-91%.
- 关键预测因素包括每天最大步数和缺乏移动辅助工具.
- 该模型展示了一致的中位预测误差 (中位绝对误差~5).
结论:
- 商用移动设备可以识别与功能能力相关的移动模式.
- 这种方法可以在不依赖临床参数的情况下对多病理患者进行BI评估.
- 人工智能驱动的可穿戴数据分析提供了一种新的方法来监测慢性疾病中的功能状态.
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