基于临床和实验室指标的机器学习算法对妊娠期肝内胆汁淤积症的预测效能

Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

作者信息

He Jianhu, Zhu Xiaojun, Yang Xuan, Wang Hui

机构信息

Information Center, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

J Matern Fetal Neonatal Med. 2025 Dec;38(1):2413854. doi: 10.1080/14767058.2024.2413854. Epub 2024 Dec 3.

DOI:10.1080/14767058.2024.2413854

PMID:39626970

Abstract

OBJECTIVES

Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention to improve the perinatal outcomes. This study aims to develop suitable machine-learning models for predicting ICP based on clinical and laboratory indicators.

METHODS

This study retrospectively analyzed data from 1092 pregnant women, with 537 diagnosed with ICP and 555 healthy cases as a control. Two study schemes were devised. For scheme 1, 62 indicators from the first period of gestation were utilized to establish predictive models. For scheme 2, 62 indicators from at least two periods of gestation were utilized to establish predictive models. Under each scheme, three different machine-learning models were developed based on the Arya Privacy Computing Platform, encompassing Support Vector Machine (SVM), Deep Neural Network (DNN), and Xgboost for Scheme 1, and Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit (GRU) for Scheme 2. The predictive efficacy of each model on ICP was evaluated and compared.

RESULTS

Under Scheme 1, the cohort comprised 1092 pregnant women (537 with ICP, 555 healthy). The SVM model exhibited a sensitivity, specificity, and accuracy of 85.5%, 47.50%, and 67.90%, respectively, while DNN showed 65.70%, 92.70%, and 79.40%, respectively, and Xgboost achieved 65.60%, 81.90%, and 73.40%, respectively. In Scheme 2, 899 pregnant women were analyzed (466 with ICP, 433 healthy). RNN demonstrated a sensitivity, specificity, and accuracy of 97.60%, 82.10%, and 90.50%, respectively; LSTM presented 90.70%, 81.70%, and 86.60%, respectively; and GRU achieved 89.90%, 83.80%, and 89.40%, respectively.

CONCLUSION

DNN and RNN are the two most suitable models to predict ICP in a convenient and available way. It provides flexible choice for medical staff and helps them optimize the therapeutic strategies to meet different clinical setting and improve the clinical prognosis of ICP.

摘要

目的

妊娠期肝内胆汁淤积症（ICP）是一种妊娠特有的疾病，需要及时识别和干预以改善围产期结局。本研究旨在基于临床和实验室指标开发适用于预测ICP的机器学习模型。

方法

本研究回顾性分析了1092例孕妇的数据，其中537例被诊断为ICP，555例健康孕妇作为对照。设计了两种研究方案。方案1中，利用妊娠早期的62项指标建立预测模型。方案2中，利用至少两个妊娠时期的62项指标建立预测模型。在每个方案下，基于Arya隐私计算平台开发了三种不同的机器学习模型，方案1包括支持向量机（SVM）、深度神经网络（DNN）和Xgboost，方案2包括递归神经网络（RNN）、长短期记忆网络（LSTM）和门控循环单元（GRU）。评估并比较了每个模型对ICP的预测效能。

结果

在方案1中，队列包括1092例孕妇（537例ICP患者，555例健康孕妇）。SVM模型的灵敏度、特异度和准确率分别为85.5%、47.50%和67.90%，而DNN分别为65.70%、92.70%和79.40%，Xgboost分别为65.60%、81.90%和73.40%。在方案2中，分析了899例孕妇（466例ICP患者，433例健康孕妇）。RNN的灵敏度、特异度和准确率分别为97.60%、82.10%和90.50%；LSTM分别为90.70%、81.70%和86.60%；GRU分别为89.90%、83.80%和89.40%。