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...

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Autori principali:	Di Wu, Sihan Wang, Cong Wang, Yijia Xiang, Lingyu Hao, Zhen Wang, Xingye Zhai, Yi Wang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2026-02-01
Serie:	Frontiers in Neurology
Soggetti:	nontraumatic subarachnoid hemorrhage machine learning length of stay hospital charges predictive modeling SHAP
Accesso online:	https://www.frontiersin.org/articles/10.3389/fneur.2026.1737503/full
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