使用机器学习预测冠状动脉疾病的老年患者7天的无计划再入院情况
Xuewu Song1,2, Yitong Tong3, Yi Luo1,2
1Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Frontiers in cardiovascular medicine
|August 24, 2023
概括
在老年冠心病 (CHD) 患者中预测非计划的7天再入院是至关重要的. 机器学习,特别是XGBoost模型,在识别高风险患者进行早期干预方面表现出很高的准确性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 短期的无计划再入院是冠心病 (CHD) 的老年患者的一个重大问题.
- 目前缺乏预测工具来预测这一人口群体中未计划的遣返.
- 这项研究解决了对有效预测模型的需求.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测老年心血管疾病患者的7天计划外再入院.
- 为此预测任务确定最有效的ML算法.
主要方法:
- 追溯收集来自老年CHD患者的临床数据.
- 实施和比较五个ML算法:XGBoost,随机森林,多层感知子,分类增强和后勤回归.
- 使用AUC,准确性,精度,回忆,F1分数,比尔分数,AUPRC和校准曲线进行性能评估. 哈普利增量解释 (SHAP) 用于模型解释.
主要成果:
- 这项研究包括834名老年CHD患者 (平均年龄73.5岁;51.08%男性).
- XGBoost (XGB) 模型表现出卓越的性能,具有最高的AUC (0.9729) 和AUPRC (0.9766).
- 由SHAP确定的关键预测因素包括骨折,高血压,停留时间,阿司匹林和D-二聚合物.
结论:
- XGBoost模型在预测老年冠状动脉疾病患者的7天计划外再入院方面非常有效.
- 开发的模型显示了临床应用在管理患者护理和减少再入院的有希望的潜力.
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