The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

Journal: The journal of trauma and acute care surgery
Published Date: Mar 1, 2022

Abstract

BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrative coding. International Classification of Diseases (ICD)-based models such as the Trauma Mortality Prediction Model (TMPM-ICD10) circumvent these limitations, but they use linear modeling, which may not adequately capture the intricate relationships of injuries on mortality. Using ICD-10 coding and machine learning (ML) algorithms, the present study used the National Trauma Data Bank to develop mortality prediction models whose performance was compared with logistic regression, ISS, and TMPM-ICD10.

Authors

  • Zachary Tran
    From the Cardiovascular Outcomes Research Laboratories (Z.T., A.V., P.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles; Division of Acute Care Surgery, Department of Surgery (Z.T., S.B.), Loma Linda University Medical Center, Loma Linda; Department of Computer Science (W.Z., R.R.), University of California, Los Angeles, California; Department of Surgery (A.C.), University of Texas Health Science Center at Tyler, Tyler, Texas; and Department of Surgery (D.K.), Harbor-UCLA Medical Center, Torrance, California.
  • Wenhao Zhang
    Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China.
  • Arjun Verma
  • Alan Cook
  • Dennis Kim
  • Sigrid Burruss
  • Ramin Ramezani
  • Peyman Benharash

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