Classification of intracranial pressure epochs using a novel machine learning framework

Patients with acute brain injuries are at risk for life threatening elevated intracranial pressure (ICP). External Ventricular Drains (EVDs) are used to measure and treat ICP, which switch between clamped and draining configurations, with accurate ICP data only available during clamped periods. Whil...

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Vydáno v:NPJ digital medicine Ročník 8; číslo 1; s. 201 - 13
Hlavní autoři: Mathur, Rohan, Yellapantula, Sudha, Cheng, Lin, Dziedzic, Peter, Potu, Niteesh, Calvillo, Eusebia, Shah, Vishank, Lefebvre, Austen, Bosel, Julian, Zink, Elizabeth K., Muehlschlegel, Susanne, Suarez, Jose I.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 11.04.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/705/1041
692/308/575
692/617/375/1345
Artificial intelligence
Biomedicine
Biotechnology
Cerebrospinal fluid
Critical care
Datasets
Hemorrhage
Hydrocephalus
Intracranial pressure
Ischemia
Machine learning
Medicine
Medicine & Public Health
Meningitis
Patients
Physiology
Pressure transducers
Traumatic brain injury
United States > US
Baltimore Maryland
ISSN:2398-6352, 2398-6352
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Popis
Shrnutí:Patients with acute brain injuries are at risk for life threatening elevated intracranial pressure (ICP). External Ventricular Drains (EVDs) are used to measure and treat ICP, which switch between clamped and draining configurations, with accurate ICP data only available during clamped periods. While traditional guidelines focus on mean ICP values, evolving evidence indicates other waveform features may hold prognostic value. However, current machine learning models using ICP waveforms exclude EVD data due to a lack of digital labels indicating the clamped state, markedly limiting their generalizability. We introduce, detail, and validate CICL ( C lassification of IC P epochs using a machine L earning framework), a semi-supervised approach to classify ICP segments from EVDs as clamped, draining, or noise. This paves the way for multiple applications, including generalizable ICP crisis prediction, potentially benefiting tens of thousands of patients annually and highlights an innovate methodology to label large high frequency physiological time series datasets.
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ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-025-01612-3