Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal: Journal of clinical epidemiology
PMID: 33359319

Abstract

OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.

Authors

  • Thomas E Cowling
    Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; Clinical Effectiveness Unit, Royal College of Surgeons of England, Lincoln's Inn Fields, London WC2A 3PE, UK. Electronic address: thomas.cowling@lshtm.ac.uk.
  • David A Cromwell
    Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; Clinical Effectiveness Unit, Royal College of Surgeons of England, Lincoln's Inn Fields, London WC2A 3PE, UK.
  • Alexis Bellot
    Department of Mathematics, University of Cambridge, Cambridge, U.K.
  • Linda D Sharples
    Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
  • Jan van der Meulen
    Department of Health Services Research & Policy, London School of Hygiene and Tropical Medicine, London, WC1H 9SH, UK; Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK.

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