基于机器学习的早产儿喂养不耐受风险预测模型的构建及 SHAP 可解释性分析。

Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.

机构信息

Department of Medical Records & Statistics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.

Department of Child and Adolescent Health, School of Public Health, Bengbu Medical University, Bengbu, Anhui, China.

出版信息

BMC Med Inform Decis Mak. 2024 Nov 18;24(1):342. doi: 10.1186/s12911-024-02751-5.

DOI:10.1186/s12911-024-02751-5

PMID:39558307

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11572196/

Abstract

OBJECTIVE

To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis.

METHODS

In this study, a sample of 350 hospitalized preterm newborns were retrospectively analysed. First, dual feature selection was conducted to identify important feature variables for model construction. Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. Finally, we apply the SHapley Additive exPlanation (SHAP) interpretable framework to analyse the decision-making principles of the optimal model and expound upon the important factors affecting FI in preterm newborns and their modes of action.

RESULTS

The accuracy of XGBoost was 87.62%, and the area under the curve (AUC) was 92.2%. After the application of tenfold cross-validation, the accuracy was 83.43%, and the AUC was 89.45%, which was significantly better than those of the other models. Analysis of the XGBoost model with the SHAP interpretable framework showed that a history of resuscitation, use of probiotics, milk opening time, interval between two stools and gestational age were the main factors affecting the occurrence of FI in preterm newborns, yielding importance scores of 0.632, 0.407, 0.313, 0.313, and 0.258, respectively. A history of resuscitation, first milk opening time ≥ 24 h and interval between stools ≥ 3 days were risk factors for FI, while the use of probiotics and gestational age ≥ 34 weeks were protective factors against FI in preterm newborns.

CONCLUSIONS

In practice, we should improve perinatal care and obstetrics with the aim of reducing the occurrence of hypoxia and preterm delivery. When feeding, early milk opening, the use of probiotics, the stimulation of defecation and other measures should be implemented with the aim of reducing the occurrence of FI.

摘要

目的

基于机器学习（ML）构建一个高度准确且可解释的喂养不耐受（FI）风险预测模型，以协助医务人员进行临床诊断。

方法

本研究回顾性分析了 350 名住院早产儿的样本。首先，进行双特征选择以确定模型构建的重要特征变量。其次，基于逻辑回归（LR）、决策树（DT）、支持向量机（SVM）和极端梯度提升（XGBoost）算法构建 ML 模型，然后分别使用随机抽样和 10 折交叉验证来评估和比较这些模型，并确定最优模型。最后，我们应用 SHapley Additive exPlanation（SHAP）可解释框架来分析最优模型的决策原则，并阐述影响早产儿 FI 的重要因素及其作用模式。

结果

XGBoost 的准确率为 87.62%，曲线下面积（AUC）为 92.2%。经过 10 折交叉验证后，准确率为 83.43%，AUC 为 89.45%，明显优于其他模型。通过 SHAP 可解释框架对 XGBoost 模型进行分析表明，复苏史、使用益生菌、开奶时间、两次排便间隔和胎龄是影响早产儿 FI 发生的主要因素，其重要性得分分别为 0.632、0.407、0.313、0.313 和 0.258。复苏史、首次开奶时间≥24 h 和排便间隔≥3 d 是 FI 的危险因素，而使用益生菌和胎龄≥34 周是早产儿 FI 的保护因素。