根据激活级别对患有慢性疾病的患者进行分类,使用机器学习方法
Onur Demiray1, Evrim D Gunes2, Ercan Kulak3
1Department of Computing, Imperial College London, London, SW7 2AZ, UK.
Health care management science
|October 12, 2023
概括
这项研究使用机器学习识别了慢性病患者的低激活率. 后勤回归最好预测低患者激活量 (PAM) 水平,突出自我监测和生活方式因素作为关键预测因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 患者参与 患者参与
背景情况:
- 患者激活措施 (PAM) 评估患者参与慢性疾病管理.
- 低PAM水平与较差的坚持,健康结果和更高的成本相关.
- 识别低PAM患者对于有针对性的干预措施至关重要.
研究的目的:
- 评估机器学习算法来分类低水平的患者激活量 (PAM).
- 为了确定预测慢性疾病中低PAM的关键患者特征.
- 为了比较各种机器学习模型和功能集的性能.
主要方法:
- 测试了八种机器学习算法:物流回归,拉索,,随机森林,梯度增强树,SVM,决策树和神经网络.
- 来自土耳其伊斯坦布尔431名患有糖尿病或高血压的成年患者的数据.
- 分析包括各种特征集,以确定患者信息的重要性.
主要成果:
- 后勤回归实现了最高的分类性能,曲线下的面积 (AUC) 为0.72.
- 低PAM的关键预测因素包括自我监测,吸烟,运动习惯,教育和社会经济地位.
- 域名知识驱动的特征选择优于自动化方法.
结论:
- 机器学习,特别是物流回归,可以有效地识别低激活水平的患者.
- 患者的自我监测,生活方式,教育和社会经济地位是激活的重要指标.
- 将领域专业知识与机器学习相结合,可以提高医疗保健中的预测准确性.
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