Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke

Abstract In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account for dynamic variables. To address this, we deve...

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Bibliografische Detailangaben

Hauptverfasser:	Ethan Phillips, Odhran O’Donoghue, Yumeng Zhang, Panos Tsimpos, Leigh Ann Mallinger, Stefanos Chatzidakis, Jack Pohlmann, Yili Du, Ivy Kim, Jonathan Song, Benjamin Brush, Stelios Smirnakis, Charlene J. Ong, Agni Orfanoudaki
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2025-05-01
Schriftenreihe:	npj Digital Medicine
Online-Zugang:	https://doi.org/10.1038/s41746-025-01687-y
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