Strokes

Comparative Analysis of Stroke Prediction Models Using Machine Learning

Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disability worldwide. This study explores the effectiveness of machine learning algorithms in predicting stroke risk using demographic, clinical, and lifestyle data from the Stroke Prediction Dataset. By addressing key methodological challenges such as cla...

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor ...

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS).

Electromechanical-assisted training for walking after stroke.

RATIONALE: Walking difficulties are common after a stroke. During rehabilitation, electromechanical and robotic gait-training devices can help improve...

Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging.

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients ...

Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.

Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increase...

AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography

Echocardiographers can detect pulmonary hypertension using Doppler echocardiography; however, accurately assessing its progression often proves chal...

[Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy].

OBJECTIVE: To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae famil...

[Pulmonary vascular interventions: innovating through adaptation and advancing through differentiation].

Pulmonary vascular intervention technology, with its minimally invasive and precise advantages, has been a groundbreaking advancement in the treatment...

Neural pathways underlying the production of pitch and rhythm in aphasia.

Singing is a universal human attribute. Previous studies suggest that the ability to produce words through singing can be preserved in poststroke apha...

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction.

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities fo...

Lipid nanoparticle (LNP) mediated mRNA delivery in neurodegenerative diseases.

Neurodegenerative diseases (NDD) are characterized by the progressive loss of neurons and the impairment of cellular functions. Messenger RNA (mRNA) h...

Lipid Ratios for Diagnosis and Prognosis of Pulmonary Hypertension.

RATIONALE: Pulmonary hypertension (PH) poses a significant health threat. Current biomarkers for PH lack specificity and have poor prognostic capabili...

Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHaple...

Usability testing a web application to support evidence-based commissioning decisions for implementing mobile stroke units.

Commissioning of innovations in healthcare is a complex socio-technical process, ideally informed by high quality evidence. However, evidence is not a...

A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction

The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it re...

GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing

Scene text editing, a subfield of image editing, requires modifying texts in images while preserving style consistency and visual coherence with the...

Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.

BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This...