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Updated: Jul 26, 2025

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Identifying inpatient mortality in MarketScan claims data using machine learning.

Fenglong Xie1,2, Timothy Beukelman2, Dongmei Sun1

  • 1Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA.

Pharmacoepidemiology and Drug Safety
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PubMed
Summary

Machine learning models accurately identify inpatient deaths using claims data. This approach overcomes missing discharge status information in large healthcare datasets, improving epidemiological research.

Keywords:
claims datamachine learningmortality

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Area of Science:

  • Health Informatics
  • Epidemiology
  • Machine Learning

Background:

  • Inpatient mortality is crucial for epidemiological studies using healthcare claims data.
  • MarketScan data began obscuring discharge status in 2016, impacting the identification of patient deaths.

Purpose of the Study:

  • To develop and validate machine learning algorithms for accurately identifying inpatient mortality.
  • To address the challenge of missing discharge status information in claims data.

Main Methods:

  • Utilized hospitalizations from 2011-2015 with missing or obscured discharge status.
  • Employed machine learning models, including Random Forest, trained on variables like age, sex, and post-discharge activity.
  • Assessed model performance using sensitivity and positive predictive value (PPV).

Main Results:

  • Over 1.3 million hospitalizations were analyzed.
  • All four machine learning methods performed well, with Random Forest achieving 88% PPV and 93% sensitivity.
  • Key predictors included lack of post-discharge claims and healthcare plan disenrollment.

Conclusions:

  • Machine learning algorithms can reliably identify inpatient mortality even with missing discharge status.
  • This methodology enables accurate analysis of inpatient deaths in obscured healthcare claims data.