基于机器学习的孕产妇预子风险评估 (PIERS-ML模型):一个建模研究
Tünde Montgomery-Csobán1, Kimberley Kavanagh1, Paul Murray2
1Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
The Lancet. Digital health
|March 22, 2024
概括
一个新的机器学习模型,PIERS-ML,准确地识别出高风险患子宫前并发症的孕妇. 这种工具有助于临床医生及时提供指导,以获得更好的孕产妇结果.
科学领域:
- 孕产妇健康 孕产妇健康
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 孕前会影响2-4%的怀孕,在全球范围内对孕产妇死亡率和发病率构成重大风险.
- 目前用于评估子宫前风险的方法可能不够准确或响应临床决策.
研究的目的:
- 开发和验证一种基于机器学习 (ML) 的新型模型,以预测患有妊娠前的妇女的不良孕产妇结果.
- 创建一个响应临床环境的工具来排除和裁决严重的孕产妇并发症.
主要方法:
- 利用来自11个国家的8843名患者的卫生系统,人口统计和临床数据进行模型开发.
- 采用随机森林ML方法,对开发数据集进行十倍交叉验证 (75%),对剩余25%进行验证.
- 对2901名英格兰住院妇女进行了外部验证;使用AUROC和概率比率评估了预测准确性.
主要成果:
- 与物流回归模型 (AUROC 0.68) 相比,PIERS-ML模型的准确性很高 (AUROC 0.80).
- 在48小时内,PIERS-ML有效地将女性分为风险组:非常低 (0%的不良事件),低 (2%),中等 (5%),高 (26%),和非常高 (91%) 的风险.
- 外部验证证实了准确的风险分类,非常低风险组的不良事件为0%.
结论:
- 皮尔斯-ML模型显著改善了鉴定孕前的妇女在最低和最大的严重不良孕产妇结果的风险.
- 这种工具可以为患者,家庭和医疗保健提供者提供准确的临床指导,从而有可能改善孕产妇的护理.
- 该模型在内部和外部验证中的表现强调了其广泛临床应用的潜力.
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