Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.

Journal: World neurosurgery

PMID: 38519020

Abstract

OBJECTIVE: This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI).

Authors

Jun Zhu

Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, 442008, China.
Yingchi Shan

Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yihua Li

Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiang Wu
Guoyi Gao

Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. Electronic address: gao3@sina.com.

Keywords

Adult Aged Algorithms Brain Injuries, Traumatic Cohort Studies Female Glasgow Coma Scale Glasgow Outcome Scale Humans Intracranial Pressure Machine Learning Male Middle Aged Patient Discharge Prognosis Prospective Studies

External Resources

View on PubMed Access via DOI PubMed (38519020)

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.

Abstract

Authors

Keywords

External Resources

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