Strokes

Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data.

Background Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic strokes. Predicting mortality of ischemic stroke patients in intensive care unit (ICU) is crucial for optimizing treatment strategies, allocating resources, and improving survival rates. Methods We acquired data on ICU ischemic...

Age-Related Differences in Risk Factors for Long-Term Care Certification in Japan: A Decision Tree Analysis Spanning 3 Years.

Ensuring healthy aging and extending health span is critical, particularly in aging societies. This study analyzed patterns of risk for long-term care (LTC) certification within 3 years in rapidly aging areas of Japan using the Kihon Checklist (KCL) and machine learning models. Data from adults aged 65 years or older in Iiyama City (aging rate 37.0%) were analyzed using Exhaustive Chi-squared Auto...

Hypertension precision medicine: the promise and pitfalls of pharmacogenomics.

Pharmacogenomics (PGx) has the potential to revolutionize hypertension management by tailoring antihypertensive therapy based on genetic profiles. Des...

Comparison of seven machine learning models in hypertension classification using photoplethysmographic and anthropometric data.

This study presents an algorithm for classifying individuals into four hypertension categories (healthy, prehypertension, Stage 1, and Stage 2) using ...

Effectiveness of Digital Health Interventions for Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD), marked by dyspnea, cough, and sputum production, significantly impairs patients' quality of ...

Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.

Atrial fibrillation (AF) is a common arrhythmia linked to increased mortality and significant healthcare burden, especially in the elderly. Direct Or...

Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As ...

Improving automatic cerebral 3D-2D CTA-DSA registration.

PURPOSE: Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT...

Older people and stroke: a machine learning approach to personalize the rehabilitation of gait.

INTRODUCTION: Stroke is a significant global public health challenge, ranking as the second leading cause of death after heart disease. One of the mos...

Genetic Risk Scores in Stroke Research and Care.

Stroke remains a leading cause of death and disability worldwide. While well-established risk factors play a major role, genetic predisposition is a c...

Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate.

Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is...

Surface Electromyography Combined with Artificial Intelligence in Predicting Neuromuscular Falls in the Elderly: A Narrative Review of Present Applications and Future Perspectives.

: Falls among the elderly are a major public health concern, leading to increased disability and mortality. Traditional protective measures are import...

Cerebral ischemia detection using deep learning techniques.

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often lea...

Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.

OBJECTIVES: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thrombo...

Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing h...

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of ...

Harnessing the hybrid machine learning methods for stroke risk classification.

Stroke is a leading global cause of death, with 80% of cases considered preventable through early detection and awareness. This study employs artifici...

Risk tools for predicting long-term sequelae based on symptom profiles after known and undetected SARS-CoV-2 infections in the population.

The aim was to determine the profile of long-term symptoms after known and undetected SARS-CoV-2 infections and to generate tools for risk and diagnos...

An adaptive feedback system for the improvement of learners.

Teachers who are aware of their students' strengths and weakness can tailor their teaching methodologies to meet the challenging students efficiently ...

Transforming tabular data into images via enhanced spatial relationships for CNN processing.

Convolutional neural networks (CNNs), renowned for their efficiency in image analysis, have revolutionized pattern and structure recognition in visual...