Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

Journal: Annals of clinical and translational neurology
PMID: 32990362

Abstract

OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients.

Authors

  • Duo Yu
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • George W Williams
    Department of Anesthesiology, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • David Aguilar
    Department of Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • José-Miguel Yamal
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • Vahed Maroufy
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • Xueying Wang
    Institute of Intelligent System and Bioinformatics, College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
  • Chenguang Zhang
    The Second Clinical College, Dalian Medical University, Dalian, Liaoning, China.
  • Yuefan Huang
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • Yuxuan Gu
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • Yashar Talebi
    Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
  • Hulin Wu
    Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States.

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