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使用机器学习预测怀孕早期流产中预期和医疗管理的结果,以开发和验证多变量临床预测模型

Sughashini Murugesu^1,2, Kristofer Linton-Reid^3,4, Emily Braun³

¹Queen Charlotte's and Chelsea Hospital, Imperial College, London, W12 0HS, UK. sughashini.murugesu@nhs.net.

BMC pregnancy and childbirth

|February 28, 2025

概括

机器学习模型使用患者数据准确预测流产管理结果. 这些模型为预期和医疗管理提供了个性化的预测,改善了患者护理.

科学领域:

生殖医学生殖医学
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

流产管理包括预期或医疗方法.
预测这些管理策略的成功对于患者咨询和护理至关重要.
现有的预测方法可能无法充分利用现有的患者数据.

研究的目的:

开发和验证机器学习 (ML) 模型,以预测预期和医疗流产管理的成功.
用现有患者,超声波和治疗结果数据评估ML模型的性能.
为了外部测试开发的ML模型的通用性.

主要方法:

对1075名接受预期或医疗流产管理的患者进行了回顾性,多地点的研究.
利用了14个预处理的功能,并在10个功能集中应用了8个ML算法.
通过训练,验证和外部测试集的接收器运行特征曲线 (AUC) 下面区域得分来评估模型性能.

主要成果:

使用LASSO进行的物流回归实现了最佳的AUC分数为0.72 (培训),0.63 (验证) 和0.70 (外部) 的预期管理.
使用RFE的后勤回归实现了医疗管理的0.64 (培训),0.62 (验证) 和0.71 (外部) 的最佳AUC得分.
模型性能在培训,验证和外部测试数据集中显示出一致性.

结论:

开发的ML模型在预测流产管理结果方面表现一致.
这些经过验证和外部测试的ML模型具有个性化结果预测的潜力.
这些发现支持使用ML来优化流产管理策略.

关键词:

预期的管理人员管理.机器学习机器学习医疗管理医疗管理流产流产流产

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