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Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.
- Source :
-
COPD: Journal of Chronic Obstructive Pulmonary Disease . Dec2019, Vol. 16 Issue 5/6, p338-343. 6p. - Publication Year :
- 2019
-
Abstract
- While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalization for Chronic Obstructive Pulmonary Disease (COPD). We identified patients aged ≥40 years with unplanned hospitalization due to COPD in the Diagnosis Procedure Combination database, an administrative claims database in Japan, from 2011 through 2016 (index hospitalizations). COPD was defined by ICD-10-CM diagnostic codes, according to Centers for Medicare and Medicaid Services (CMS) readmission measures. The primary outcome was any readmission within 30 days after index hospitalization. In the training set (randomly-selected 70% of sample), patient characteristics and inpatient care data were used as predictors to derive a conventional logistic regression model and two machine learning models (lasso regression and deep neural network). In the test set (remaining 30% of sample), the prediction performances of the machine learning models were examined by comparison with the reference model based on CMS readmission measures. Among 44,929 index hospitalizations for COPD, 3413 (7%) were readmitted within 30 days after discharge. The reference model had the lowest discrimination ability (C-statistic: 0.57 [95% confidence interval (CI) 0.56–0.59]). The two machine learning models had moderate, significantly higher discrimination ability (C-statistic: lasso regression, 0.61 [95% CI 0.59–0.61], p = 0.004; deep neural network, 0.61 [95% CI 0.59–0.63], p = 0.007). Tube feeding duration, blood transfusion, thoracentesis use, and male sex were important predictors. In this study using nationwide administrative data in Japan, machine learning models improved the prediction of 30-day readmission after COPD hospitalization compared with a conventional model. [ABSTRACT FROM AUTHOR]
- Subjects :
- *OBSTRUCTIVE lung diseases
*PREDICTION models
*INPATIENT care
*HOSPITAL care
Subjects
Details
- Language :
- English
- ISSN :
- 15412555
- Volume :
- 16
- Issue :
- 5/6
- Database :
- Academic Search Index
- Journal :
- COPD: Journal of Chronic Obstructive Pulmonary Disease
- Publication Type :
- Academic Journal
- Accession number :
- 139806370
- Full Text :
- https://doi.org/10.1080/15412555.2019.1688278