深度生存分析用于可解释的时间变化的预测孕前风险
Braden W Eberhard1, Kathryn J Gray2, David W Bates3
1Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Journal of biomedical informatics
|July 13, 2024
概括
深度学习生存模型准确地预测怀孕期间的妊娠前风险,通过识别个性化的风险轨迹和早期干预的独特风险因素,优于传统方法.
科学领域:
- 医学统计 医学统计
- 医疗保健中的机器学习
- 生殖健康研究 生殖健康研究
背景情况:
- 传统的生存分析,就像考克斯比例危险模型一样,假定比例风险,这在复杂的医疗条件下经常被侵犯.
- 与怀孕相关的并发症,如孕前,在整个妊娠期间表现出时间变化的危险因素,挑战了经典的生存模型.
- 深度学习生存模型提供了先进的能力来处理不成比例的危险和复杂的时间动态.
研究的目的:
- 开发和评估一种深度学习方法来建模子宫前的时间风险.
- 通过先进的生存分析来确定与子宫前相关的临床风险因素.
- 为了比较深度生存模型的表现与预测子宫前的传统方法.
主要方法:
- 利用了2015年至2023年期间66,425例怀孕的回顾性数据集.
- 修改了DeepHit的深度生存模型,以捕捉怀孕数据中的时间变化的关系.
- 应用时间序列k-平均集群和沙普利值来进行风险分层和可解释性.
主要成果:
- 在处理高维数据和不断变化的危险方面,DeepHit表现出与考克斯比例危险模型相比的性能 (AUC 0.78).
- 深度生存模型成功地确定了孕前的时间变化的风险轨迹,提供了个性化的干预见解.
- K-意味着将划分出来的患者分为低风险,早期发病和晚期发病的孕前组,每组都有独特的风险因素.
结论:
- 深度生存分析提供了一个强大的工具,用于时间变化的预测孕前风险.
- 深度学习模型通过提供个性化的风险轨迹和可解释的临床见解,比传统方法提供优势.
- 这项研究强调了深度生存模型在孕产妇健康方面的临床应用的潜力.
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