Mortality risk assessment and interpretability analysis of preterm infants in the ICU by using machine learning models
10.3969/j.issn.1673-9701.2025.18.007
- VernacularTitle:结合机器学习模型的早产儿ICU死亡风险评估与可解释性分析
- Author:
Yanfeng SU
1
;
Suru HONG
;
Yushuang CHEN
;
Xiayang WU
Author Information
1. 厦门医学院附属第二医院急诊医学科,福建厦门 361021
- Publication Type:Journal Article
- Keywords:
Preterm infants;
ICU mortality risk;
Machine learning;
Light gradient boosting machine model;
Risk prediction
- From:
China Modern Doctor
2025;63(18):32-36
- CountryChina
- Language:Chinese
-
Abstract:
Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.
