Modelling hospital readmissions under frailty conditions for healthy aging

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.

Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare

“…Machine learning and other statistical methods have been applied to predict the risk of 30-day readmission in older adults. 44,[50][51][52][53][54] In 2020, Grana et al 50 conducted experiments in a cohort of 645 frail patients for the study of readmission showing positive results for the application of machine learning and making the case for more studies in larger cohorts such as ours. Another study reports that frail geriatric trauma and emergency general surgery patients tend to have longer lengths of stay and more readmissions.…”

Improving decision making in the management of hospital readmissions using modern survival analysis techniques

“…This has been further motivated by their lack of distributional assumptions and their greater ability to capture highly non-linear and complex relationships compared to traditional techniques. Prominent machine learning techniques have included artificial neural networks (ANNs) [2,24,36,65,68], support vector machines (SVMs) [8,56,69], random forests [15,20,27], and decision trees [48,63]. In general, more complex techniques have been found to improve on logistic regression, though generalisation of results is made difficult by differences in datasets, patient groups, and conditions across studies [6].…”