Deep learning prediction of hospital readmissions for asthma and COPD.

Journal: Respiratory research
PMID: 38093373

Abstract

QUESTION: Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and evaluate the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data.

Authors

  • Kevin Lopez
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA.
  • Huan Li
    National Clinical Research Center for Kidney Disease, State Key Laboratory for Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
  • Zachary Lipkin-Moore
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA.
  • Shannon Kay
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA.
  • Haseena Rajeevan
    Biomedical Informatics and Data Science, Yale University, New Haven, CT, 06520, USA.
  • J Lucian Davis
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA.
  • F Perry Wilson
  • Carolyn L Rochester
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA.
  • Jose L Gomez
    Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, CT, 06520-8057, USA. jose.gomez-villalobos@yale.edu.

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