Deep learning prediction of hospital readmissions for asthma and COPD

Autor: Kevin Lopez, Huan Li, Zachary Lipkin-Moore, Shannon Kay, Haseena Rajeevan, J. Lucian Davis, F. Perry Wilson, Carolyn L. Rochester, Jose L. Gomez
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Respiratory Research, Vol 24, Iss 1, Pp 1-11 (2023)
Druh dokumentu: article
ISSN: 1465-993X
DOI: 10.1186/s12931-023-02628-7
Popis: 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. Study design and methods Observational study between September 30, 2012, and December 31, 2017, of patients hospitalized with asthma and COPD exacerbations. Results This study included 5,794 patients, 1,893 with asthma and 3,901 with COPD. Patients with asthma were predominantly female (n = 1288 [68%]), 35% were Black (n = 669), and 25% (n = 479) were Hispanic. Black (44 vs. 33%, p = 0.01) and Hispanic patients (30 vs. 24%, p = 0.02) were more likely to be readmitted for asthma. Similarly, patients with COPD readmissions included a large percentage of Blacks (18 vs. 10%, p
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje