Why the study?
Hospital readmission for chronic illness is a ubiquitous phenomenon and a major contributor to growing healthcare costs.
How can machine learning techniques be utilized to predict hospital readmissions for CHF and COPD patients?
How can machine learning techniques be utilized to predict hospital readmissions for CHF and COPD patients?
Machine learning models predicting readmissions for CHF and COPD should incorporate previous visit data and longitudinal tracking of patients.
Incorporate prior visits and longitudinal tracking into ML readmission models for CHF and COPD; extends systematic evidence on predictive performance.
Hospital readmission for chronic illness is a ubiquitous phenomenon that is a major contributor to the growing costs of the healthcare sector. Here, PRISMA was used to identify studies dealing with predicting readmissions for CHF and COPD patients that implemented machine learning techniques. The PRISMA output yielded 21 articles that met the inclusion criteria. It is recommended to include previous visit data, and track the same patients over multiple visits when predicting these readmissions.
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Ofir Ben‐Assuli (2021) studied this question.
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