通过不规则重复的电子健康记录的机器学习建模改进心血管风险预测
Chaiquan Li1, Xiaofei Liu1, Peng Shen2
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China.
使用电子健康记录的机器学习模型显著改善了动脉样硬化心血管疾病 (ASCVD) 风险预测. 这些先进的算法比传统方法更好地识别高风险个体.
科学领域:
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 电子健康记录 (EHR) 包含有关风险因素的有价值的纵向数据.
- 在EHR中不规则测量数据对传统风险预测模型构成挑战.
研究的目的:
- 利用机器学习 (ML) 算法来提高动脉样硬化心血管疾病 (ASCVD) 风险预测,使用EHR数据.
- 为了实现自动对ASCVD风险进行人口查.
主要方法:
- 利用了基于EHR的纵向队列研究中的215,744名中国成年人 (40-79岁) 的数据.
- 应用极端梯度提升 (XGBoost) 和最小绝对收缩和选择运算符 (LASSO) 回归模型.
- 包括人口,药物,脂质,血糖,肥胖,血压和功能数据用于模型解释性.
主要成果:
- XGBoost模型的C-统计数据比中国-PAR Cox模型高 (0.792).
- 与传统模型相比,Lasso回归也显示出更好的预测准确性.
- 机器学习算法展示了优异的校准和净重新分类改进,更有效地识别高风险个体.
结论:
- 机器学习算法有效地利用不规则的,现实世界的EHR数据来改进心血管风险预测.
- ML模型显著提高了个体的重新分类,从而更好地识别了ASCVD高风险人群.
更多相关视频
05:51Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
05:03Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
Published on: December 11, 2019
相关概念视频
Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Assessment of the Cardiovascular System I: Subjective Data
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
Errors occurring during blood pressure monitoring
Several factors...
Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
