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Updated: Jan 14, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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使用可解释的机器学习算法预测子宫外妊娠.

Arkan Aghayari¹, Amir Sorayaie Azar², Mortaza Taheri-Anganeh³

¹Reproductive Health Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran.

BMC pregnancy and childbirth

|October 22, 2025

概括

机器学习模型可以预测子宫外妊娠 (EP) 风险. 关键因素包括周期中期疼痛,生殖器手术史和经期障碍,改善早期检测和患者的结果.

关键词:

异卵性怀孕异卵性怀孕异卵性怀孕可以解释性解释性机器学习算法机器学习算法预测预测预测

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科学领域:

生殖健康生殖健康
医疗信息学医疗信息学
机器学习在医学中的应用

背景情况:

宫外怀孕 (EP) 发生在胚胎囊在子宫外植入时.
准确的风险识别和理解风险因素关系对于管理EP至关重要.

研究的目的:

开发预测模型,以提高异胎妊娠风险识别.
揭示已知的风险因素与异胎妊娠发生之间的关系.

主要方法:

使用五倍交叉验证和网格搜索进行模型优化.
使用精度,AUC和NPV评估模型性能.
在特征显著性分析中使用了夏普利添加式解释 (SHAP).

主要成果:

随机森林 (RF) 实现了最高的性能 (87.13%准确率,90.65%AUC).
通过物流回归 (LR) 和SHAP分析确定了周期中期疼痛,生殖器手术史和经期不良作为关键预测因素.

结论:

机器学习模型显示了改善异胎妊娠临床决策的潜力.
需要在不同的群体中进行进一步的验证,考虑到诸如单一中心数据和未评估的风险因素 (例如,PID,IUD) 等局限性.