Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke

Objective: This study aimed to develop and validate a machine learning-based short measure to assess 5 functions (the ML-5F) (activities of daily living [ADL], balance, upper extremity [UE] and lower extremity [LE] motor function, and mobility) in patients with stroke. Design: Secondary data from a previous study. A follow-up study assessed patients with stroke using the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) at hospital admission and discharge. Setting: A rehabilitation unit in a medical center. Participants: Patients (N=307) with stroke. Interventions: Not applicable. Main Outcome Measures: The BI, PASS, and STREAM. Results: A machine learning algorithm, Extreme Gradient Boosting, was used to select 15 items from the BI, PASS, and STREAM, and transformed the raw scores of the selected items into the scores of the ML-5F. The ML-5F demonstrated good concurrent validity (Pearson's r, 0.88-0.98) and responsiveness (standardized response mean, 0.28-1.01). Conclusions: The ML-5F comprises only 15 items but demonstrates sufficient concurrent validity and responsiveness to assess ADL, balance, UE and LE functions, and mobility in patients with stroke. The ML-5F shows great potential as an efficient outcome measure in clinical settings. (c) 2021 by the American Congress of Rehabilitation Medicine.

Automated summary

This study aimed to develop and validate a machine learning-based short measure (ML-5F) for assessing five functions in stroke patients. The ML-5F, consisting of 15 selected items transformed into scores using the Extreme Gradient Boosting algorithm, demonstrated good concurrent validity and responsiveness. It has the potential to be an efficient outcome measure in clinical settings for evaluating activities of daily living, balance, upper extremity and lower extremity motor function, and mobility in stroke patients.