Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms-specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893-0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Prognostic Equations and Accuracy of a Total Score of Functional Independence Measure at Discharge for Different Diseases in a Convalescent Rehabilitation Ward

OBJECTIVES

Prognosis and goal setting from admission in the convalescent rehabilitation ward, supported by a multidisciplinary team, enhance rehabilitation and discharge support. Predicting functional independence measure (FIM) outcomes can further optimize these processes. This study aimed to develop prognostic equations for the motor FIM at discharge for stroke, hip fracture (HF), vertebral compression fractures (VCFs), and total knee arthroplasty (TKA), which are common diseases in patients admitted to convalescent rehabilitation wards, using multiple regression analysis, and to clarify the difference in the accuracy of the predicted motor FIM according to the disease.

METHODS

This study included 965 patients admitted to our hospital. The objective variable consists of the motor FIM at discharge, and the explanatory variables were age, sex, days from onset to admission, total admission motor FIM, and total admission cognitive FIM. A stepwise multiple regression analysis was performed. The analysis of the difference in the accuracy of predicted motor FIM by disease used the absolute value of the residuals.

RESULTS

The total motor FIM and cognitive FIM at admission were extracted for all four diseases included in this study. The absolute value of the residuals appeared to be more accurate for TKA, HF, stroke, and VCF in that order.

CONCLUSIONS

Although differences in the accuracy of the prediction equation were observed by disease, this prediction equation can be used as an approach to review the details of rehabilitation and discharge and can be tailored to each case.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.