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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| 主要な著者: | , , , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Frontiers Media S.A.
2026-02-01
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| シリーズ: | Frontiers in Neurology |
| 主題: | |
| オンライン･アクセス: | https://www.frontiersin.org/articles/10.3389/fneur.2026.1737503/full |
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