A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

Journal: The journal of trauma and acute care surgery
PMID: 32773672

Abstract

BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We hypothesized machine learning could be applied to critically ill patients and would outperform currently used mortality scores.

Authors

  • Fahad Shabbir Ahmed
    From the Department of Pathology (F.S.A.), Yale School of Medicine, Yale University, New Haven, Connecticut; School of Information and Communication Engineering (L.A.), University of Electronic Science and Technology of China (UESTC), Chengdu, China; Department of Electrical Engineering (L.A.), University of Science and Technology, Bannu, Pakistan; Division of Trauma, Acute Care, Burn, and Emergency Surgery (B.A.J.), University of Arizona, Tucson, Arizona; Department of Neurology (A.I.), University of New Mexico, Albuquerque, New Mexico; Department of Computer Science (R.-u.-M.), COMSATS University Islamabad, Islamabad, Pakistan; and Division of Computer Science, Mathematics, and Science (Healthcare Informatics) (S.A.C.B.), St. John's University, New York, New York.
  • Liaqat Ali
    School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China.
  • Bellal A Joseph
  • Asad Ikram
  • Raza Ul Mustafa
  • Syed Ahmad Chan Bukhari
    Department of Pathology, Yale School of Medicine, New Haven, CT, USA. ahmad.chan@yale.edu.

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