Strokes

Lightweight Electronic Signatures and Reliable Access Control Included in Sensor Networks to Prevent Cyber Attacks from Modifying Patient Data

Digital terrorism is a major cause of securing patient/healthcare providers data and information. Sensitive topics that may have an impact on a patient's health or even national security include patient health records and information on healthcare providers. Health databases and data sets have been continually breached by many, regular assaults, as well as local and remote servers equipped with ...

PASS: Private Attributes Protection with Stochastic Data Substitution

The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Various studies have been proposed to protect private attributes by removing them from the data while maintaining the utilities of the data for downstream tasks. Nevertheless, as we theoretically and empirically show in th...

A pilot study for self-guided, active robotic training of proprioception of the upper limb in chronic stroke.

BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments are not typically addressed during assessment or r...

HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation

Stroke rehabilitation often demands precise tracking of patient movements to monitor progress, with complexities of rehabilitation exercises present...

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalizat...

Development of a Diagnostic Prediction Model for Post-Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol.

OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive impairment (PSCI) significantly affects long-term pr...

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.

OBJECTIVE: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can p...

A Combined-Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke.

Acute minor ischemic stroke patients often experience recurrence shortly after symptom onset, highlighting the importance of predicting stroke recurre...

Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With MRI.

BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fa...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment pr...

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 ...

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standar...

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is imposs...

Enhanced neuroplasticity and gait recovery in stroke patients: a comparative analysis of active and passive robotic training modes.

BACKGROUND: Stroke is a leading cause of long-term disability, with lower limb dysfunction being a common sequela that significantly impacts patients'...

A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke

Length of stay (LOS) prediction in acute stroke is critical for improving care planning. Existing machine learning models have shown suboptimal pred...

DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with elevated health risks, where timely detection is pivotal for mitigating s...

Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model.

The aim of this study was to compare the performance of 4 machine learning models-Lasso regression model, random forest model, Boruta algorithm model,...

Early efficacy observation of suspended lower-limb rehabilitation robot-assisted therapy in patients with intensive care unit-acquired weakness: a study protocol for a self-controlled randomised controlled trial.

INTRODUCTION: Intensive care unit-acquired weakness (ICUAW) is a common and severe complication in critically ill patients, associated with high morbi...

The analysis of motion recognition model for badminton player movements using machine learning.

This study aims to comprehensively analyze and classify the badminton players' swing actions by combining the theoretical frameworks of quantum mechan...

Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services.

Cardiovascular disease (CVD) is one of the severe disorders that requires effectual solutions. CVD mainly affects heart functionality in the human bod...