Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Richard Moberg; Ethan Jacob Moyer; Dai Wai Olson; Eric Rosenthal; Brandon Foreman

doi:10.1007/s12028-022-01524-0

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Richard Moberg, Ethan Jacob Moyer, Dai Wai Olson, Eric Rosenthal, Brandon Foreman

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

Original language	English (US)
Pages (from-to)	202-205
Number of pages	4
Journal	Neurocritical Care
Volume	37
DOIs	https://doi.org/10.1007/s12028-022-01524-0
State	Published - Aug 2022

Keywords

Big data
Critical care
Data annotation
Informatics
Physiologic monitoring
Standards

ASJC Scopus subject areas

Clinical Neurology
Critical Care and Intensive Care Medicine

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10.1007/s12028-022-01524-0

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abstract = "Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.",

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AU - Rosenthal, Eric

AU - Foreman, Brandon

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AB - Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the “readiness” of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

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Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Abstract

Keywords

ASJC Scopus subject areas

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