通过数据挖掘提出一种HELLP综合征预测模型。

Presenting a prediction model for HELLP syndrome through data mining.

作者信息

Farajollahi Boshra, Sayadi Mohammadjavad, Langarizadeh Mostafa, Ajori Ladan

机构信息

Department of Health Information Management, School of Health Management and Information Sciences, University of Medical Sciences, Tehran, Iran.

Department of Computer Engineering, University of Applied Science and Technology (UAST), Tehran, Iran.

出版信息

BMC Med Inform Decis Mak. 2025 Mar 17;25(1):135. doi: 10.1186/s12911-025-02904-0.

DOI:10.1186/s12911-025-02904-0

PMID:40098129

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11916871/

Abstract

BACKGROUND

The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pregnancy-related disorders is complicated. Furthermore, late diagnosis leads to a delay in treatment, which challenges disease management. The present study aimed to present a machine learning (ML) attitude for diagnosing HELLP syndrome based on non-invasive parameters.

METHOD

This cross-sectional study was conducted on 384 patients in Tajrish Hospital, Tehran, Iran, during 2010-2021 in four stages. In the first stage, data elements were identified using a literature review and Delphi method. Then, patient records were gathered, and in the third stage, the dataset was preprocessed and prepared for modeling. Finally, ML models were implemented, and their evaluation metrics were compared.

RESULTS

A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). According to the modeling output, some variables, such as platelet, gestational age, and alanine aminotransferase (ALT), were the most important in diagnosing HELLP syndrome.

CONCLUSION

The present work indicated that ML algorithms can be used successfully in the development of HELLP syndrome diagnosis models. Other algorithms besides DTs have an F1 score above 0.90. In addition, this study demonstrated that biomarker features (among all features) have the most significant impact on the diagnosis of HELLP syndrome.

摘要

背景

HELLP综合征代表三种并发症：溶血、肝酶升高和血小板计数降低。由于HELLP综合征的病因和发病机制尚未完全明确，将其与其他妊娠相关疾病区分开来较为复杂。此外，诊断延迟会导致治疗延误，给疾病管理带来挑战。本研究旨在提出一种基于非侵入性参数诊断HELLP综合征的机器学习方法。

方法

这项横断面研究于2010年至2021年期间在伊朗德黑兰塔吉里什医院对384例患者分四个阶段进行。在第一阶段，通过文献综述和德尔菲法确定数据元素。然后，收集患者记录，并在第三阶段对数据集进行预处理以准备建模。最后，实施机器学习模型并比较其评估指标。

结果

第一阶段后，本研究共纳入21个变量。在所有机器学习算法中，多层感知器和深度学习表现最佳，F1分数超过99%。在5折和10折交叉验证的所有三种评估场景中，K近邻（KNN）、随机森林（RF）、AdaBoost、XGBoost和逻辑回归（LR）的F1分数超过0.95，而支持向量机（SVM）的该值约为0.90，决策树（DT）的最低值低于0.90。根据建模输出，一些变量，如血小板、孕周和丙氨酸转氨酶（ALT），在诊断HELLP综合征中最为重要。