Aug 2026· The Laryngoscope· Vol 136, pp. 4755 - 4764· 0 citations· 28 references
Medicine
TL;DR
Machine learning models, particularly XGBoost, provide robust risk stratification for high-risk neonates and can complement existing programs by identifying high-risk infants who require prioritized diagnostic follow-up and closer clinical monitoring.
Abstract
This study aims to develop and evaluate a machine learning‐based risk stratification tool for predicting hearing loss in high‐risk neonates using clinical risk factors to support targeted early identification.
ML models can distinguish high-risk KC groups based on clinical risk factors, facilitating risk stratification and early lifestyle interventions and confirmed by univariable logistic regression.
Kaiyue Du, R. Peng, Yueguo Chen et al.· PLoS ONE· 0 citations
Preeclampsia is a major cause of maternal and perinatal morbidity and mortality worldwide, and early identification of high-risk pregnancies remains a clinical challenge, particularly in settings where advanced biomarkers and imaging techniques are not readily available. This study aimed to develop and evaluate a machi...
U. Dogan, Şeyma Yaşar, E. Comert et al.· Medical Science· 0 citations
A machine learning model based on routinely available data is developed and validated to support early risk stratification and clinical decision-making in resource-limited environments and indicates that machine learning models, particularly LR, may be useful for PHD risk screening and stratification among patients wit...
Yi-Fei Xie, Yi Feng, Xing-Lin Wan et al.· Frontiers in Public Health· 0 citations
Introduction Retinopathy of prematurity (ROP) remains an important cause of preventable visual impairment in preterm infants. Current screening relies primarily on gestational age and birth weight, which may not fully capture the heterogeneous postnatal clinical course of very preterm infants. This study aimed to devel...
Si-Yu Chen, Shu-Yue Deng, Xin-Yi Liu et al.· Frontiers in Pediatrics· 0 citations
Abstract Background Despite advances in perinatal care and improved survival of preterm infants, bronchopulmonary dysplasia (BPD) remains a major neonatal morbidity with long-term consequences. Accurate early risk prediction is therefore clinically important. Conventional models may be limited in capturing the complex...
Ye-Quan Xu, Ying Zhang, Dan-Ni Ye et al.· Annals medicus· 0 citations
LightGBM shows the best overall performance for risk stratification of PONV after craniotomy, and SHAP visualization endows the model with favorable clinical interpretability to facilitate individualized risk prediction.
Ling-Yi Wu, Hong-Ying Pan, Yi-Hong Xu et al.· Frontiers in Medicine· 0 citations
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