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These models are built once and may predict readmission for any patient at any hospital.
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The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk.
Boulding et al. [15] have used LR to predict readmission using patient satisfaction as independent variables.
Research by Braga et al. used SVM to predict readmission for intensive care patients, while Sushmita et al. have used SVM for prediction of all-cause readmission [9, 17].
Identification of factors that might predict readmission after bariatric surgery could help surgeons target high-risk patients.
Additional factors that predict readmission risk and advanced machine learning techniques should be considered to improve readmission models.
Physiological risk factors predict readmission, but the impact of modifiable psychosocial risk factors remains uncertain.
Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months.
3– 7 Several studies tried to identify patients at risk, but they could only moderately predict mortality and poorly predict readmission.
We examined three outcomes of interest, depending on unplanned readmission diagnosis, which helped elucidate background factors that predict readmission events in COPD patients.
Badawi and Breslow developed two tools to respectively predict readmission and mortality 48 hours post-ICU discharge.
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