一种广泛适用的方法,通过识别患者以完整的数据来丰富电子健康记录队列:多站点评估
Jeffrey G Klann1,2, Darren W Henderson3, Michele Morris4
1Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 26, 2023
概括
研究人员开发了一个"忠诚度评分"来识别医疗保健系统中拥有完整电子健康记录 (EHR) 的患者. 这种方法通过尽量减少EHR数据中缺少的信息来提高医学研究数据的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 临床研究 临床研究
背景情况:
- 在单一医疗保健系统内接受护理的患者,称为"忠诚队伍",拥有更完整的电子健康记录 (EHR) 数据.
- 不完整的EHR数据可以在研究结果中引入无意的偏见.
- 识别这些高完整度队列对于准确的因果分析至关重要.
研究的目的:
- 实施和验证一个可计算的程序,用于识别使用EHR数据的忠诚群体.
- 评估机器学习模型对忠诚群体识别的预测能力.
- 为研究机构提供一个工具,以选择具有高数据完整性的患者队列.
主要方法:
- 开发了在i2b2平台上实施的机器学习模型,以识别忠诚群体.
- 使用常规护理的代理和医疗保健利用指标进行评分.
- 实施了一种新的验证方法,仅使用EHR数据来评估患者返回率.
主要成果:
- 忠诚度评分算法在接收器操作特征曲线下的平均面积为0.77 (原始模型) 和0.80 (校准模型).
- 诸如多种药物和访问等因素在所有评估地点都是显著的预测因素.
- 查测试贡献的特定地点变化被观察到,可能是由于编码和人口差异.
结论:
- 忠诚度评分的开源实现表明了良好的预测性能.
- 这种算法可以选择具有高EHR数据完整性的患者队列.
- i2b2网站可以利用这种方法来提高研究中因果推理的准确性.
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