AIMC Topic: Stroke

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Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study.

International journal of medical informatics Feb 22, 2025
BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke ...
Bayes Theorem Patient Discharge Stroke Patient Readmission Aged, 80 and over Humans Male ROC Curve Prospective Studies Female Aged Machine Learning
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Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning.

Biosensors Feb 22, 2025
Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigat...
Humans Machine Learning Silver Biomarkers Stroke Spectrum Analysis, Raman Metal Nanoparticles
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Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

Computers in biology and medicine Feb 21, 2025
BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatmen...
Neural Networks, Computer Middle Aged Machine Learning Humans Electrocardiography Risk Factors Atrial Fibrillation Stroke Prospective Studies Aged Aged, 80 and over Retrospective Studies Male Female
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Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

Aging clinical and experimental research Feb 20, 2025
BACKGROUND: Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no standardised tool exists for its screening in this population. This study aims to identify factors influe...
Stroke Early Diagnosis Middle Aged Machine Learning Risk Assessment Female Aged Sarcopenia Aged, 80 and over Male Humans
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Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery.

Journal of neuroscience methods Feb 18, 2025
BACKGROUND: For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS approach enhances the neuroplasticity and facilitates in functional Stroke Rehabilitation (SR) thro...
Treatment Outcome Neuronal Plasticity Recovery of Function Stroke Adult Male Middle Aged Stroke Rehabilitation Transcranial Direct Current Stimulation Transcranial Magnetic Stimulation Humans Female Aged Machine Learning Brain
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Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.

Journal of neurointerventional surgery Feb 14, 2025
BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).
Male Middle Aged Deep Learning Retrospective Studies Cohort Studies Stroke Tomography, X-Ray Computed Female Humans Endovascular Procedures Treatment Outcome Aged Thrombectomy Prognosis Predictive Value of Tests Aged, 80 and over Computed Tomography Angiography Ischemic Stroke
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Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought.

Artificial intelligence in medicine Feb 12, 2025
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 ...
Humans Electronic Health Records Natural Language Processing Stroke Knowledge Bases Large Language Models
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

BMC medical informatics and decision making Feb 7, 2025
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...
Principal Component Analysis Stroke Boosting Machine Learning Algorithms Artificial Intelligence Machine Learning Risk Assessment Humans
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Using machine learning to predict outcomes following transcarotid artery revascularization.

Scientific reports Jan 31, 2025
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...
Treatment Outcome Stroke Middle Aged Risk Assessment Postoperative Complications Humans Aged, 80 and over Machine Learning Risk Factors Male Female Aged
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Machine learning to predict stroke risk from routine hospital data: A systematic review.

International journal of medical informatics Jan 28, 2025
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 ...
Risk Assessment Humans Hospitals Machine Learning Stroke Risk Factors
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