Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage

BackgroundNontraumatic subarachnoid hemorrhage (SAH) is a critical condition requiring prolonged hospitalization and significant healthcare costs. Identifying factors contributing to extended length of stay (LOS) and predicting associated hospital charges can optimize clinical decision-making and re...

Description complète

Détails bibliographiques

Auteurs principaux:	Di Wu, Sihan Wang, Cong Wang, Yijia Xiang, Lingyu Hao, Zhen Wang, Xingye Zhai, Yi Wang
Format:	Artigo
Langue:	Inglês
Publié:	Frontiers Media S.A. 2026-02-01
Collection:	Frontiers in Neurology
Sujets:	nontraumatic subarachnoid hemorrhage machine learning length of stay hospital charges predictive modeling SHAP
Accès en ligne:	https://www.frontiersin.org/articles/10.3389/fneur.2026.1737503/full
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!