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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedLopez K, Li H, Lipkin-Moore Z
Deep learning prediction of hospital readmissions for asthma and COPD.
The purpose of this observational study was to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and assess the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data. The study included 31, 2017 patients hospitalized with asthma and COPD exacerbations. The study found that Black and Hispanic patients had a greater likelihood of readmission for asthma. Patients with COPD readmissions included a high percentage of Blacks and Hispanics. To identify patients at high risk of readmission, index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. The researchers discovered that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset.
AHRQ-funded; HS027626.
Citation: Lopez K, Li H, Lipkin-Moore Z .
Deep learning prediction of hospital readmissions for asthma and COPD.
Respir Res 2023 Dec 13; 24(1):311. doi: 10.1186/s12931-023-02628-7..
Keywords: Asthma, Respiratory Conditions, Hospital Readmissions, Electronic Health Records (EHRs), Health Information Technology (HIT)