A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead

Abstract

To date, the assessment of disability in older people is obtained utilizing a Comprehensive Geriatric Assessment (CGA). However, it is often difficult to understand which areas of CGA are most predictive of the disability. The aim of this study is to evaluate the possibility to early predictone year ahead-the disability level of a patient using Machine Leaning models. Methods Community-dwelling older people were enrolled in this study. CGA was made at baseline and at 1 year follow-up. After collecting input/independent variables (i.e. age, gender, schooling followed, Body Mass Index, information on smoking, polypharmacy, functional status, cognitive performance, depression, nutritional status), we performed two distinct Support Vector Machine models (SVMs) able to predict functional status one year ahead. In order to validate the choice of the model the results achieved with the SVMs were compared with the output produced by simple Linear Regression (LR) models. Results 218 patients (mean age =78.01; sd=7.85; Male= 39%) were recruited. The combination of the two SVMs is able to achieve a higher prediction accuracy (exceeding 80% instances correctly classified vs 67% instances correctly classified by the combination of the two linear regression models). Furthermore, SVMs are able to classify both the three categories, Self Sufficienty, Disability Risk and Disability, while linear regression model separates the population only in two groups (Self Sufficiency and Disability) without identifying the intermediate category (Disability Risk) which turns out to be the most critical one. Conclusions The development of such a model can contribute to the early detection of patients at risk of self-sufficiency loss.

Prediction of emergence from prolonged disorders of consciousness from measures within the UK rehabilitation outcomes collaborative database: a multicentre analysis using machine learning

“…Verrusio et al. [ 17 ] used Comprehensive Geriatric Assessment (CGA) measures to predict patient disability levels one year ahead. Their SVMs achieved higher predictive accuracy for patients in three classes (self-sufficiency, disability risk, and disability) than linear regression models (84% vs 67%, respectively).…”

“…developed an MLA that showed promise for predicting which patients responded to one antidepressant rather than another [ 16 ]. In rehabilitation studies, MLAs have been used for guiding planning for home care clients, predicting risk of acute care readmission among rehabilitation inpatients and predicting functional status of community-dwelling older people 12 months later [ 17–19 ]. Other applications of ML in rehabilitation have included: decision tree models to predict ventilator associated pneumonia after traumatic brain injury (TBI), accelerometer-based algorithms to classify physical activity after acquired brain injury (ABI), and prediction of outcomes after hip fracture [ 20–22 ].…”