Neurology

Predicting stroke severity of patients using interpretable machine learning algorithms.

BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the Nation...

Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

BACKGROUND: Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.

Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present ...

The Value of Machine Learning Models in Predicting Factors Associated with the Need for Permanent Shunting in Patients with Intracerebral Hemorrhage Requiring Emergency Cerebrospinal Fluid Diversion.

OBJECTIVE: To assess the efficacy of machine learning models in identifying factors associated with the need for permanent ventricular shunt placement...

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in thei...

CBMAFF-Net: An Intelligent NMR-Based Nontargeted Screening Method for New Psychoactive Substances.

With the proliferation and rapid evolution of new psychoactive substances (NPSs), traditional database-based search methods face increasing challenges...

Data-driven explainable machine learning for personalized risk classification of myasthenic crisis.

OBJECTIVE: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring intensive care treatment and invasive therapies. Clas...

Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024).

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impa...

Expert level of detection of interictal discharges with a deep neural network.

OBJECTIVE: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalograp...

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition...

Generalizable self-supervised learning for brain CTA in acute stroke.

Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks ...

Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algor...

Machine learning-based predictive model for post-stroke dementia.

BACKGROUND: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Ma...

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and ...

Grade prediction of lesions in cerebral white matter using a convolutional neural network.

We established a diagnostic method for cerebral white matter lesions using MRI images and examined the relationship between the MRI images and the med...

A deep learning model of dorsal and ventral visual streams for DVSD.

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networ...

Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.

BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) mode...

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.

The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG s...

Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.

BACKGROUND: Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic ...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current...