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

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.

OptiSelect and EnShap: Integrating Machine Learning and Game Theory for Ischemic stroke prediction

“…However, combining GBT and Cox models, the ensemble approach outperformed individual models, boasting higher accuracy, specificity, and positive predictive value. These findings highlight the potential of integrating machine learning, specifically ensemble models, in clinical practice for identifying high-risk individuals susceptible to stroke [26][27][28][29] . Moreover, machine learning has been extensively explored for long-term stroke recurrence prediction in ischemic stroke patients.…”

Possibilities neuroimaging and neurophysiological research methods to objectify rehabilitation potential in patients with ischemic stroke (analytical review of the literature)

“…При этом должны быть валидированы все модели и представлены показатели эффективности, охватываю щие два ключевых вопроса: дискриминацию и калибровку [50]. Поиск предикторов функционального восстановления после инсульта с помощью машинного обучения (автоматического распознавания образа) по данным 19 исследований также не показал каких-либо однозначных результатов в связи с гетерогенностью и малыми объемами выборок, способами оценки, различиями в концепции поиска этих факторов [51]. В исследованиях, включенных в поиск, оценивались качество жизни, функциональная независимость, степень выраженности неврологических нарушений, в частности гемипареза, атаксии, нарушений чувствительности, бульбарных нарушений и множества других симптомов и синдромов, что не позволило привести их к общему знаменателю.…”

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma

“…12 Prognostic models based on ML have been developed for several different diseases, such as acute stoke, depression, and malignancies. [13][14][15] However, ML-based models to predict the outcomes of patients with early-stage HCC have not been widely studied.…”