Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review

Wanting Zu, Xuemiao Huang, Tianxin Xu, Lin Du, Yiming Wang, Lisheng Wang, Wenbo Nie

PLoS ONE 2023 review cited by 27

Source Open-access PDF More in Medicine & Health

importance 5/10 ai assisted Stroke Rehabilitation and Recovery stroke rehabilitation stage: research

Problem

Assessing whether machine learning can reliably predict which stroke patients will benefit from rehabilitation treatment.

Why it matters

Stroke rehabilitation planning depends on reliable outcome prediction, affecting patient recovery and resource use.

What the AI did

Systematic review (PRISMA, CHARMS, PROBAST) of 10 studies and 32 ML predictive models for stroke rehabilitation outcomes.

Results

Optimal AUC ranged 0.63-0.91 and R2 0.64-0.91; all 32 models rated high or unclear risk of bias

Impact

Review concludes existing ML models are biased and unreliable; much room for improvement remains.

Record

Venue	PLoS ONE
Published	2023
Work type	review
Authors	Wanting Zu, Xuemiao Huang, Tianxin Xu, Lin Du, Yiming Wang, Lisheng Wang, Wenbo Nie
Citations	27 (OpenAlex, at import time)
References	55
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1371/journal.pone.0287308
Open access	gold
Keywords	machine learning, stroke rehabilitation, systematic review, PROBAST, risk of bias

Importance rating

5 / 10

real but narrower advance

Rehabilitation outcome prediction matters for recovery planning and resource use, but the review finds all 32 models at high or unclear risk of bias, so current reliable capability is limited.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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