Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review

 I'd fire everyone involved in useless predicting failure to recover research like this.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review

• Silvia Campagnini,
• Chiara Arienti,
• Michele Patrini,
• Piergiuseppe Liuzzi,
• Andrea Mannini &
• Maria Chiara Carrozza 

Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 54 (2022) Cite this article

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Abstract

Background

Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis. In this framework, the objective of this work is to develop a systematic review of machine learning algorithms as solutions to predict motor functional recovery of post-stroke patients after treatment.

Methods

We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outcome assessed, the method for feature extraction and selection, the algorithm used, and the validation approach. The methodological quality of included studies was assessed using the prediction model risk of bias assessment tool (PROBAST). A qualitative description of the characteristics of the included studies as well as a narrative data synthesis was performed.

Results

A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the methods, linear and logistic regressions were the most frequently used and cross-validation was the preferred validation approach.

Conclusions

We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elements prevented a quantitative comparison across models, we defined the most frequently used models given a specific outcome, providing useful indications for the application of more complex machine learning algorithms in rehabilitation medicine.

Background

Vascular problems in nature are the leading cause of death, and stroke is ranked second in worldwide mortality [1]. It accounted for 5.5 million deaths in 2006 [2]. Indeed, for survivors, the burden of stroke is producing an increase in the number of disability-adjusted living years (DALYs). For this reason, the ultimate challenge in stroke rehabilitation research is to improve the rehabilitation protocols by tuning them according to an optimised early outcome prognosis [3]. Therefore, advances in artificial intelligence, machine learning (ML), and more generically data-driven tools, may have a central role in rehabilitation decision-making and protocol development. ML is the methodology that provides computers with the ability to learn from experience. By designing and training algorithms able to learn decision rules from data, automatic solutions able to make predictions on new data can be exploited [4].

ML algorithms have been used often in recent years to predict clinical outcomes [5]. The recent growing interest is due to the increasing complexity and numerosity of available data sets, as well as the presence of multifactorial data with diverse origins, for which more classical methods do not allow accurate results [6, 7].

From this perspective and given the available technologies, a new concept of rehabilitation is arising, namely “Rehabilomics”. This innovative view of the rehabilitative intervention concerns a multifactorial data-driven evaluation of the patient, aiming at the identification of physiological, genetic, biochemical or metabolic biomarkers as factors concurring in the rehabilitation process. The correlation of these biomarkers with the clinical outcome that measures the recovery of the patient could lead to important information for rehabilitation treatment planning.

Considering the latest advances in ML-based predictive models could be employed to promote the development of personalised rehabilitation processes for individual recovery. This would result in a human-centred framework in which the synergy among therapies, biogenetics, imaging techniques, technological devices and data-driven tools has a key role [8].

In the literature, there has been a broad exploration of solutions for outcome prediction in medicine applications [6, 9,10,11], and very few of them are about ML models in stroke rehabilitation [12, 13]. Most of the reviews in this field provide only a narrative description of the studies, without providing a systematic analysis of the results. On the other hand, those prioritising the technical aspects of the models often lack a clinical contextualisation of the findings. For example, Christodoulou et al. [6], ML methods for clinical outcome prediction are compared across pathologies without providing details about the outcomes used. So, although the review was highly detailed from the technical point of view, i.e. regarding the algorithms validation approaches and performance metrics used, the clinical aspects were out of focus. We are convinced that a proper discussion of the results in light of the clinical context (i.e., pathology and measures) in which they are obtained is essential for translational applicability of the solutions developed, from research to the clinical practice.

Thus, there is an urgent need for a study able to integrate and combine clinical and engineering/technical aspects of predictive solutions used in rehabilitation. The aim of this study is to identify the predictive solutions, developed using ML or theory-based algorithms and internally or externally validated, used for functional outcome prognosis in stroke patients after a rehabilitation programme. The predictive solutions were investigated comprehensively, by evaluating their technical characteristics and performances in association with the clinical selection of input and output variables.

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