Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series

Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. This study presents a proof-of-concept machine learning framework, the Multi-source Irregu...

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Bibliographic Details
Main Authors:	Hadi Mehdizavareh, Arijit Khan, Simon Lebech Cichosz
Format:	Article
Language:	English
Published:	Elsevier 2025-01-01
Series:	Computational and Structural Biotechnology Journal
Subjects:	Electronic health record Multi-source learning Irregular time series Next glucose level prediction ICU patients
Online Access:	http://www.sciencedirect.com/science/article/pii/S2001037025002545
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Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series

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