In what multiverse do you live where predicting failure to recover is of any use at all to survivors? And your mentors and senior researchers approved this crapola?
Machine Learning Techniques for the Prediction of Functional Outcomes in the Rehabilitation of Post-Stroke Patients: A Scoping Review
1
Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
2
Department of Neurology, School of
Medicine, University Hospital of Alexandroupolis, Democritus University
of Thrace, 68100 Alexandroupolis, Greece
*
Author to whom correspondence should be addressed.
BioMed 2023, 3(1), 1-20; https://doi.org/10.3390/biomed3010001
Received: 10 November 2022
/
Revised: 18 December 2022
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Accepted: 21 December 2022
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Published: 27 December 2022
Abstract
Stroke is one of the main causes of long-term
disabilities, increasing the cost of national healthcare systems due to
the elevated costs of rigorous treatment that is required, as well as
personal cost because of the decreased ability of the patient to work.
Traditional rehabilitation strategies rely heavily on individual
clinical data and the caregiver’s experience to evaluate the patient and
not in data extracted from population data. The use of machine learning
(ML) algorithms can offer evaluation tools that will lead to new
personalized interventions. The aim of this scoping review is to
introduce the reader to key directions of ML techniques for the
prediction of functional outcomes in stroke rehabilitation and identify
future scientific research directions. The search of the relevant
literature was performed using PubMed and Semantic Scholar online
databases. Full-text articles were included if they focused on ML in
predicting the functional outcome of stroke rehabilitation. A total of
26 out of the 265 articles met our inclusion criteria. The selected
studies included ML approaches and were directly related to the
inclusion criteria. ML can play a key role in supporting decision making
during pre- and post-treatment interventions for post-stroke survivors,
by utilizing multidisciplinary data sources.
1. Introduction
Stroke, as a result of either a sudden brain blood supply interruption or local brain blood vessel eruption [1],
may cause paralysis (plegia) or weakness (paresis), with detrimental
consequences on daily activities, such as dressing, eating, and walking,
as well as problems with memory, cognition, speaking, emotion control,
numbness, and pain [2,3,4].
An aging population and the interaction of risk factors enhance the
risk of stroke, leading to an increased number of people with long-term
disabilities [3,5].
Forms of stroke rehabilitation include a mixture of pharmacologic and
nonpharmacologic interventions that target physiological and functional
deficits; however, traditional one-size-fits-all approaches often leave a
considerable portion of patients without effective treatment. The
design of effective interventions has proven a challenging task due to
the high variability of patients’ level of impairment and symptoms [6,7,8].
Hence, the development of personalized assessment/prognostic tools that
could lead to better risk stratification and prediction of functional
outcomes, based on past and current data, is necessary. Innovative,
evidence-based strategies that can utilize longitudinal, multisource
population data could aid individual rehabilitation by shaping
personalized interventions, both at admission and throughout the
patient’s path of care.
To this end, classical
statistical approaches such as linear regression have been employed to
model post-stroke rehabilitation and predict functional outcomes, using a
binary (good or poor) classification or specific score outcomes. These
models, which are typically based on standard scales and relevant
clinical data, fail to incorporate meaningful factors that are
detrimental to patient-specific recovery pathways, such as the level of
family or community support and the cultural level. On the contrary,
advanced artificial intelligence (AI)-based correlation models, such as
machine learning algorithms, can analyze large, inhomogeneous datasets,
map nonlinearities between multiple input and output variables, and
extract patterns among various clinical outcomes.
ML
is the study of how machines (i.e., learning algorithms) can learn
patterns or complex relationships from daily data and produce trained
mathematical models linking target variables of interest with a huge
number of covariates. Furthermore, deep learning (DL) is defined as a
subfield of ML concerned with learning algorithms inspired by the
function and structure of the brain [7].
DL provides an alternative architecture system by overcoming the burden
of feature engineering. Hence, ML has the ability to cope with
high-dimensionality data and complex cases [9,10],
going beyond the traditional statistical approaches and overcoming
significant limitations in the prediction of the functional outcome in
the rehabilitation of post-stroke survivors, offering valuable tools in
the field of stroke rehabilitation [3].
Currently,
various ML techniques have been employed to model individual disease
pathways, and their contribution plays a key role in the scientific
community. For example, in the case of knee osteoarthritis, several
ML-based patient-specific prediction models have been developed (e.g.,
ML models for diagnosis, post-treatment assessment, and segmentation in
knee osteoarthritis) [11]. Moreover, ML demonstrated excellent performances in predicting the outcome for several neurosurgical conditions [12,13]. In addition, Bivard et al. demonstrated the importance of AI and imaging in stroke management [14].
Furthermore, they presented the need for AI tools for the quick
assessment of meaningful imaging data and to support clinical decisions.
In
this context, the current scoping review was carried out to (i)
investigate ML methods employed to predict functional outcome in stroke
rehabilitation, (ii) identify current trends in this field, and (iii)
identify the existing literature gap for future scientific approaches.
Compared to the already available literature on the field of post-stroke
rehabilitation [15],
this paper focuses specifically on the various ML models being used to
predict functional outcomes in stroke rehabilitation, as well as their
specific characteristics and underlying principles. By providing an
in-depth examination of these models, the paper aims to shed light on
the current state of the field and identify trends and areas for future
research. Overall, the primary aim of the paper is to provide a
comprehensive overview of the ML approaches being used in this area of
study.
More at link.
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