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Stroke

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Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke.

Prehospital emergency care
OBJECTIVES: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of health care, particularly for its potential applications in ...

Optimizing Stroke Detection Using Evidential Networks and Uncertainty-Based Refinement.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Evaluating neurological impairments post-stroke is essential for assessing treatment efficacy and managing subsequent disabilities. Conventional clinical assessment methods depend largely on clinicians' visual and physical evaluations, resulting in c...

Effects of robot-assisted gait training within 1 week after stroke onset on degree of gait independence in individuals with hemiparesis: a propensity score-matched analysis in a single-center cohort study.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robot-assisted gait training (RAGT) is an effective method for treating gait disorders in individuals with stroke. However, no previous studies have demonstrated the effectiveness of RAGT in individuals with acute stroke. This study aimed...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

The American journal of cardiology
Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural blee...

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

International journal of medical informatics
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 ...

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Scientific reports
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

Computers in biology and medicine
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...

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

Aging clinical and experimental research
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...

Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery.

Journal of neuroscience methods
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...