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预测早产使用可解释的机器学习在一个有前性和多次性孕妇的潜在队列中预测早产

Wasif Khan^1,2, Nazar Zaki^1,2, Nadirah Ghenimi³

¹Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE.

|December 27, 2023

概括

机器学习模型可以预测早产 (PTB) 并识别关键风险因素. 像SHAP这样的可解释AI方法可以帮助临床医生了解个体风险,有助于更好地照顾孕妇.

科学领域:

产科和妇科产科和妇科
医疗人工智能医疗人工智能
公共卫生公共卫生

背景情况:

过早分娩 (PTB) 是围产和长期婴儿发病的主要原因.
目前用于PTB预测的机器学习 (ML) 模型缺乏临床解释性.
了解风险因素对于有针对性的干预和改善孕产妇和胎儿的结果至关重要.

研究的目的:

在阿联人口中开发和验证用于PTB预测的ML模型.
用SHapley添加式扩展 (SHAP) 识别显著的PTB风险因素.
为临床决策支持提供可解释的预测.

主要方法:

分析了阿联3509名孕妇的数据集.
评估了六个ML算法,其中XGBoost表现出卓越的性能.
应用SHAP和LIME用于特征归属和个体预测解释.

主要成果:

对于PTB预测,XGBoost的AUC为0.735 (缓慢) 和0.723 (无缓慢).
确定了关键的风险因素:以前的PTB,剖腹产,孕前,孕产妇年龄 (parous);BMI,孕产妇年龄,胎儿感染 (nulliparous).
整体PTB发生率为11.23%.

结论:

开发的ML模型显示了作为阿联人口中PTB查工具的潜力.
SHAP和LIME分析提高了对个人PTB风险因素的临床理解.
可解释的人工智能可以支持临床医生管理PTB风险并减少不良结果.

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