Modelling hospital readmissions under frailty conditions for healthy aging. Issue 2 (1st July 2019)

• Record Type: Journal Article
• Authors: Graña, Manuel; Lopez‐Guede, Jose Manuel; Irazusta, Jon; Labayen, Idoia; Besga, Ariadna
• Is Part Of: Expert systems. Volume 37:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-01
• Subjects: data driven -- frailty -- healthy aging -- machine learning -- method -- readmission prediction; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12437
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: In the current context of an aging population in many developed countries, the issue of healthy aging is at the forefront of the political, scientific, and technological concerns. The frailty accompanying the late years of elderly people (>70 years old) deserves special consideration due to its great economical and personal costs and the workload imposed on the health care system. Hospital readmissions under a short time after hospital discharge are one of the sources of concern, and much effort is being devoted to their prediction for better care of the elder and optimized resource management. In this paper, we consider the prediction of readmissions for patients that are evaluated positively in the frailty scales. The computational experiments are carried out over a gender‐balanced cohort of 645 patients recruited at the University Hospital of Alava. We report machine‐learning prediction results of the readmission before the standard readmission limit of 30 days. We apply an upsampling technique to correct for class imbalance. Results are positive, encouraging further research and the creation of larger cohorts in international efforts.