Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke

 I personally think all these predictions of recovery are total bullshit.  Current full recovery only occurs 10% of the time, so all that is being done is predicting failure to recover. And that is totally fucking useless for survivors.

Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke

• Wan-Wen Liao,
• Yu-Wei Hsieh,
• Tsong-Hai Lee,
• Chia-ling Chen &
• Ching-yi Wu 

Scientific Reports volume 12, Article number: 11235 (2022) Cite this article

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Abstract

Health related quality of life (HRQOL) reflects individuals perceived of wellness in health domains and is often deteriorated after stroke. Precise prediction of HRQOL changes after rehabilitation interventions is critical for optimizing stroke rehabilitation efficiency and efficacy. Machine learning (ML) has become a promising outcome prediction approach because of its high accuracy and easiness to use. Incorporating ML models into rehabilitation practice may facilitate efficient and accurate clinical decision making. Therefore, this study aimed to determine if ML algorithms could accurately predict clinically significant HRQOL improvements after stroke sensorimotor rehabilitation interventions and identify important predictors. Five ML algorithms including the random forest (RF), k-nearest neighbors (KNN), artificial neural network, support vector machine and logistic regression were used. Datasets from 132 people with chronic stroke were included. The Stroke Impact Scale was used for assessing multi-dimensional and global self-perceived HRQOL. Potential predictors included personal characteristics and baseline cognitive/motor/sensory/functional/HRQOL attributes. Data were divided into training and test sets. Tenfold cross-validation procedure with the training data set was used for developing models. The test set was used for determining model performance. Results revealed that RF was effective at predicting multidimensional HRQOL (accuracy: 85%; area under the receiver operating characteristic curve, AUC-ROC: 0.86) and global perceived recovery (accuracy: 80%; AUC-ROC: 0.75), and KNN was effective at predicting global perceived recovery (accuracy: 82.5%; AUC-ROC: 0.76). Age/gender, baseline HRQOL, wrist/hand muscle function, arm movement efficiency and sensory function were identified as crucial predictors. Our study indicated that RF and KNN outperformed the other three models on predicting HRQOL recovery after sensorimotor rehabilitation in stroke patients and could be considered for future clinical application.

Introduction

Health related quality of life (HRQOL) refers to the way an individual feels and reacts to his/her health status affected by medical conditions¹. Compared to quality of life that covers all aspects of well-beings of human life, HRQOL focuses more on well-beings related to health domains such as physical, functional and mental health and it has been regarded as an important outcome of treatments^1,2.

Stroke remains a leading cause of long-term disability³. It has a wide-ranging impact not only on physical and daily function but also on HRQOL⁴. Most patients still suffered from deteriorated HRQOL even in the chronic phase of stroke, which makes HRQOL an crucial target for stroke rehabilitation^4,5. To improve patients’ HRQOL, healthcare professionals have to provide rehabilitation interventions that are most effective for each patient based on his/her responses to that rehabilitation therapy. Building accurate prediction models for forecasting patients’ HRQOL improvements after rehabilitation interventions and identifying predictors relevant for HRQOL improvements in stroke patients are thus imperative for providing insights to healthcare professionals on making accurate clinical decision.

Machine learning (ML) has become a popular prediction analytic approach. Machine learning uses automatic computerized algorithms to discover patterns in the data and builds prediction models to forecast future events. Machine learning is particularly suitable for predicting health outcomes because it can process large volumes of data, analyze the complex relationship between various different features/variables and easily incorporate new variables into prediction models without re-adjusting the preprogrammed rules⁶. In addition, the feature selection procedure can be incorporated into machine learning procedures to help identify important predictors⁷. These advantages make machine learning a potentially ideal tool for realizing accurate outcome prediction in patient populations.

In stroke, machine learning has been primarily used for predicting motor and activities of daily living (ADL) recovery and has achieved an overall positive result^8,9,10,11,12. However, to our knowledge, only one study to date has applied machine learning algorithms in predicting stroke-specific HRQOL recovery¹³. In that study, the authors incorporated six demographic factors into machine learning models and built a preliminary system to forecast HRQOL changes of chronic stroke patients. Small prediction errors (i.e., the root mean square errors) were found between the data derived from the prediction model and the actual data collected from the patient, suggesting that machine learning might be feasible for predicting HRQOL changes in chronic stroke patients¹³.

Despite this positive evidence, the previous study only included demographic attributes into the machine learning prediction model¹³; nevertheless, HRQOL has been shown to be affected by factors across multiple domains including demographic as well as health-related domains such as physical and functional domains^4,5,14. Including only demographic attributes in the machine learning model may not be sufficient for optimizing prediction accuracy. In addition, the previous study only examined prediction errors (e.g., the mean squared error) of the machine learning model¹³. Important clinical performance metrics such as prediction accuracy and the ability of machine learning models to distinguish between responders and non-responders to rehabilitation interventions remain largely unexplored¹⁵. A comprehensive examination of machine learning prediction performance along with factors across health domains is required for determining the efficacy of machine learning on predicting HRQOL recovery of stroke patients after rehabilitation interventions.

Stroke sensorimotor rehabilitation interventions including the robot-assisted therapy (RT), mirror therapy (MT) and transcranial direct current stimulation (tDCS) have become popular approaches for improving stroke recovery in the recent decade. These three approaches (i.e., RT, MT and tDCS) use modern equipment/modalities (e.g., robotic arms, mirror boxes and electrical stimulators) to modulate peripheral and/or central sensorimotor systems (e.g., visuomotor and sensorimotor systems and cortical areas) to augment stroke recovery^16,17,18. Several studies have demonstrated that these three sensorimotor interventions (i.e., RT, MT and tDCS) not only facilitated functional recovery but also improved participation and HRQOL in stroke patients^{19,20,21,22,23,24,25}. The rationale of why these three sensorimotor interventions (i.e., RT, MT and tDCS) could improve HRQOL is that these interventions could reduce arm/hand impairment, restore arm/hand function, which would allow stroke patients to participate in daily activities and accomplish essential daily tasks^{19,20,21,22,23,24,25}. Most daily tasks such as bathing, dressing, dining and grocery shopping all involve use of the arm/hand to manipulate objects to accomplish tasks. Good arm/hand function would lead to successful participation in daily tasks and subsequently may increase stroke patients’ subjective feeling of well-beings and satisfaction toward daily life^{19,20,21,22,23,24,25}. Thus, these three interventions (i.e., RT, MT and tDCS) may have potentials to be incorporated into current clinical practice to facilitate not only functional recovery but also HRQOL in stroke patients. Machine learning may be a potentially useful tool for predicting HRQOL changes after these three interventions, which may help identify responders to these three interventions and facilitate clinical application^6,7.

Therefore, the purpose of this study was to determine the performance of machine learning algorithms on predicting clinically significant HRQOL improvements of chronic stroke patients after stroke sensorimotor rehabilitation interventions including the RT, MT and tDCS. We examined the performance of five commonly used machine learning algorithms and identified important predictors for building machine learning prediction models.