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使用机器学习方法预测死胎的情况

Woo Jeng Kim¹, Sae Kyung Choi¹, Yun Sung Jo²

¹Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea.

Scientific reports

|October 15, 2025

概括

一个机器学习模型有效地预测单子妊娠中死产风险,使用28周之前收集的数据. 这种工具有助于评估未来孕妇的个人风险,特别是在东亚人口中.

科学领域:

围产儿医学围产儿医学
医疗保健中的人工智能
生殖健康研究生殖健康研究

背景情况:

死产仍然是一个重要的全球健康问题,每年影响着许多怀孕.
预测模型对于早期识别和干预来降低死产率至关重要.
现有的预测方法往往缺乏准确性或不适合特定的人口群体.

研究的目的:

开发和验证用于预测死产风险的机器学习模型.
用SHAP值来识别关键的预测变量,以简化模型.
评估模型在一个大型,多中心的单子妊娠队列中的表现.

主要方法:

从韩国多中心对32,953例单独怀孕的回顾性分析.
使用基线,E1 (13周前) 和T0 (28周前) 数据开发极端梯度增强机模型.
使用曲线下的面积 (AUC) 和精度回忆曲线下的面积 (AUPR) 的指标进行验证,并通过SHAP值进行模型简化.

主要成果:

该模型对所有死胎 (AUC 0.720-0.740) 和晚期死胎 (AUC 0.781) 的预测性能良好.
晚期死产的简化模型实现了可比性能 (AUC 0.759),突出了关键预测变量.
这些模型使用28周妊娠前可用的数据显示了有效性.

结论:

开发的机器学习模型为预测单子妊娠中晚期死产风险提供了一个有希望的工具.
该模型的实用性可能特别适用于东亚人口.
使用这种模型进行早期风险评估可以促进及时干预并改善围产期结果.

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