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Stroke

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Machine learning to predict stroke risk from routine hospital data: A systematic review.

International journal of medical informatics
PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there ...

Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.

Clinical neurology and neurosurgery
BACKGROUND: Stroke remains a leading cause of death and disability worldwide, with African populations bearing a disproportionately high burden due to limited healthcare infrastructure. Early prediction and intervention are critical to reducing strok...

Using machine learning to predict outcomes following transcarotid artery revascularization.

Scientific reports
Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed...

Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.

Journal of neuroscience methods
BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment indi...

Comparative efficacy of robot-assisted therapy associated with other different interventions on upper limb rehabilitation after stroke: A protocol for a network meta-analysis.

PloS one
INTRODUCTION: Post-stroke movement disorders are common, especially upper limb dysfunction, which seriously affects the physical and mental health of stroke patients. With the continuous development of intelligent technology, robot-assisted therapy h...

Ledged Beam Walking Test Automatic Tracker: Artificial intelligence-based functional evaluation in a stroke model.

Computers in biology and medicine
The quantitative evaluation of motor function in experimental stroke models is essential for the preclinical assessment of new therapeutic strategies that can be transferred to clinical research; however, conventional assessment tests are hampered by...

A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.

Molecular genetics and genomics : MGG
Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of str...

Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought.

Artificial intelligence in medicine
BACKGROUND: Understanding and extracting valuable information from electronic health records (EHRs) is important for improving healthcare delivery and health outcomes. Large language models (LLMs) have demonstrated significant proficiency in natural ...

Integrating Artificial Intelligence in Stroke Rehabilitation: Current Trends and Future Directions; A mini review.

JPMA. The Journal of the Pakistan Medical Association
Rehabilitation following a stroke faces challenges in offering customized treatment and attaining the best possible outcomes. The utilization of artificial intelligence (AI) presents transformative solutions that have the potential to revolutionize e...

Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

BMC medical informatics and decision making
The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of...