Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children.

Ackerman, Kassi; Mohammed, Akram; Chinthala, Lokesh; Davis, Robert L; Kamaleswaran, Rishikesan; Shafi, Nadeem I

Ackerman, Kassi; Mohammed, Akram; Chinthala, Lokesh; Davis, Robert L; Kamaleswaran, Rishikesan; Shafi, Nadeem I.

Affiliation

Ackerman K; University of Tennessee Health Science Center, Memphis, TN, USA.
Mohammed A; University of Tennessee Health Science Center, Memphis, TN, USA.
Chinthala L; University of Tennessee Health Science Center, Memphis, TN, USA.
Davis RL; University of Tennessee Health Science Center, Memphis, TN, USA.
Kamaleswaran R; Emory University School of Medicine, Atlanta, GA, USA.
Shafi NI; Georgia Institute of Technology, Atlanta, GA, USA.

Sci Rep ; 12(1): 21473, 2022 12 12.

Article in En | MEDLINE | ID: mdl-36509794

Subject(s)

Intracranial Hypertension; Intracranial Pressure; Child; Humans; Intracranial Pressure/physiology; Blood Pressure; Intracranial Hypertension/diagnosis; ROC Curve; Machine Learning

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Intracranial Pressure / Intracranial Hypertension Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Child / Humans Language: En Journal: Sci Rep Year: 2022 Document type: Article

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