基于贝叶斯网络的模型的开发和验证,用于从电子健康记录中预测冠心病风险
Xiaowen Suo1,2, Xiaocheng Huang3, Lu Zhong1,2
1Department of Biostatistics School of Public Health, Cheeloo College of Medicine, Shandong University Jinan Shandong China.
Journal of the American Heart Association
|December 29, 2023
概括
一个新的风险预测模型有效地处理电子健康记录中常见的丢失和被审查的数据. 这种方法可以改善冠心病风险评估,从而改善患者的治疗结果.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 传统的风险评估模型面临着电子健康记录 (EHR) 中缺少和被审查的数据的挑战.
- 现有的方法需要对电子健康记录数据进行特殊考虑,这些数据经常包含不完整的信息.
- 开发可靠的EHR数据预测模型对于准确的公共卫生和临床指导至关重要.
研究的目的:
- 开发和验证冠心病 (CHD) 风险的高性能预测模型.
- 应对电子健康记录中缺少和被审查的数据所带来的挑战.
- 增强EHR数据对临床决策和患者结果改善的有用性.
主要方法:
- 在海市立医院对169,692名参与者 (2013-2021) 的回顾性队列研究.
- 使用人口,实验室和病史变量开发心血管疾病风险模型.
- 分别使用完整样本和剩余样本进行模型培训和验证 (n=110,325和n=59,367).
主要成果:
- 该模型在导出队列中达到0.800的接收器运行特征曲线 (AUC) 下的面积,在验证队列中达到0.837.
- C-统计是0.796 (衍生) 和0.838 (验证),表明强大的预测性能.
- 校准曲线和决策曲线分析证实了该模型的良好校准和临床有用性.
结论:
- 拟议的风险预测模型有效地管理了EHR固有的缺失和被审查的数据.
- 这种经过验证的模型表明,通过改进EHR数据的利用,可以显著提高患者的治疗结果.
- 这些发现表明,依靠EHR数据的临床实践和公共卫生倡议是一个有价值的工具.
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