Identifying inpatient mortality in MarketScan claims data using machine learning.

Journal: Pharmacoepidemiology and drug safety
PMID: 37344984

Abstract

PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values to missing. We used a machine learning approach to correctly identify hospitalizations that resulted in inpatient death using data prior to 2016.

Authors

  • Fenglong Xie
    Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Timothy Beukelman
    Foundation for Science, Technology, Education, and Research (FASTER), Birmingham, Alabama, USA.
  • Dongmei Sun
    Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Huifeng Yun
    Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Jeffrey R Curtis
    Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.

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