[ARTICLE] Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches – Full Text

Abstract

Background

Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models.

Methods

This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models.

Results

Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77.

Conclusions

Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

Introduction

Stroke is one of the leading causes of long-term disability [1]. Most stroke patients suffer from upper limb hemiparesis that significantly impairs their functional abilities and quality of life [2]. To help patients restore function, healthcare professionals have to provide rehabilitation interventions that are effective for each patient based on predicted outcomes. Nevertheless, making accurate prediction remains to be a challenging task due to the heterogeneous characteristics and recovery patterns among stroke patients [3].

With the recent advancement in technology, new techniques have been developed to assist clinicians/therapists in predicting patient recovery. One promising new technique is machine learning. Machine learning utilizes computerized algorithms to optimize prediction. It has several advantages including the ability to process large volumes of data, detection of complex interactions between multiple variables and easy incorporation of new attributes/data into models [4]. These advantages make machine learning an ideal tool for processing complex healthcare informatics data to develop prediction models [5].

In stroke, machine learning techniques have been used for predicting motor and functional recovery in acute/subacute stroke patients. For example, Lin et al. evaluated whether machine learning models could predict recovery of activities of daily living in acute stroke patients [6]. Other studies assessed whether machine learning models could predict motor and/or cognitive improvement in acute/subacute stroke patients [7,8,9]. Results of these studies were promising with moderate to high accuracy; however, these studies primarily involved inpatient rehabilitation in acute/subacute stroke. Whether the machine learning methods can predict responses of stroke patients to outpatient rehabilitation interventions, such as contemporary task-oriented interventions at chronic stage of stroke remain unknown.

Contemporary task-oriented rehabilitation interventions including the constraint-induced movement therapy (CIMT), bilateral arm training (BAT), robot-assisted therapy (RT) and mirror therapy (MT) are commonly used to address motor dysfunction in chronic stroke patients [10]. Systematic reviews and meta-analysis studies showed that these contemporary interventions were effective in improving motor function in chronic stroke patients, and should be considered in clinical application [11,12,13,14]. Machine learning may be a useful tool to predict motor function improvement after contemporary task-oriented interventions, which may help to identify the responders to these interventions and facilitate practical use.

The purpose of this study was to determine the accuracy and performance of machine learning in predicting clinically significant motor function improvement after contemporary task-oriented interventions in chronic stroke patients and identify important predictors for building machine learning prediction models.[…]

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