从电子健康记录数据预测孕前发病时间的五个特征模型:开发和验证研究研究
Hailey K Ballard1,2, Xiaotong Yang1, Aditya D Mahadevan3,4
1Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States.
Journal of medical Internet research
|August 14, 2024
概括
现在使用电子健康记录更准确地预测孕前的发病. 结合母亲特征和实验室数据的定量模型改善了预产前的妊娠年龄的预测.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 孕前是一种危险的妊娠并发症,其特点是高血压和蛋白尿.
- 准确预测孕前发作仍然是一个重大的临床挑战.
研究的目的:
- 开发定量模型来预测妊娠年龄,预先怀孕.
- 使用电子健康记录 (EHR) 进行预测建模.
主要方法:
- 追溯收集了1178 (发现) 和881 (验证) 的孕前怀孕记录.
- 构建两个考克斯比例危险模型:一个基线模型和一个包括实验室发现,生命体征和药物在内的完整模型.
- 模型开发使用80%的发现数据,对20%进行测试,并与外部数据进行验证.
主要成果:
- 在发现数据中,与基线模型 (0.64) 相比,完整模型显示出更好的预测性能 (一致性指数为0.69).
- 在34周和37周预测妊娠前的曲线下面面积 (AUC) 值为完整模型的0.70.
- 关键预测因素包括胎儿数量,高血压,平价,以及怀孕早期的透静血压.
结论:
- 对于预测子宫前妊娠年龄而言,EHR数据非常有价值.
- 具有5个预测因子的Cox比例危险模型为临床医生提供了评估孕前发病风险的实用工具.
关键词:
欧洲人权理事会 欧洲人权理事会电子健康记录是电子健康记录.医疗记录 医疗记录机器学习是机器学习.母亲的母亲的母亲.死亡率 死亡率孕前症是什么意思怀孕 怀孕 怀孕 怀孕 怀孕预后 预后 预后危险的风险 危险的风险风险预测风险预测幸存率 幸存率 生存率生存分析,生存分析.更多相关视频
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