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Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.

BMC nephrology
BACKGROUND: Protein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim ...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting.

BMC primary care
BACKGROUND: Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the p...

Evaluation of anthropometric and ultrasonographic measurements with different machine learning methods in predicting difficult intubation: a prospective observational study.

BMC anesthesiology
INTRODUCTION: Difficult intubation is one of the most challenging scenarios to deal with due to increased morbidity and mortality. Machine learning systems can help predict this process in advance. This study aimed to predict whether patients had dif...

AI-generated images of familiar faces are indistinguishable from real photographs.

Cognitive research: principles and implications
Human users are now able to generate synthetic face images with artificial intelligence (AI) tools. Although indistinguishable from real photographs, these images have tended to feature fictional identities that do not exist in the real world. As a r...

Educational robotics as a pedagogical resource for K-12 students with learning difficulties.

Scientific reports
Learning difficulties in children can have a variety of causes, often interconnected. Factors such as emotional problems (anxiety, stress), communication difficulties, inadequate teaching methodologies, health problems, lack of family support, and ev...

Determinants of student adoption of artificial intelligence applications in higher education.

Scientific reports
The integration of artificial intelligence (AI) into educational settings has the potential to transform learning experiences; however, its adoption among students remains impacted by various factors. The current study assesses the determinant factor...

Resting state EEG reveals no reliable biomarkers of tinnitus laterality.

Scientific reports
This study assessed whether resting-state quantitative EEG (qEEG) can differentiate tinnitus laterality under rigorous multiple-comparison control and nested, cross-validated machine learning (ML). We analyzed 210 pre-specified qEEG features-spectral...

A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.

Scientific reports
Alzheimer's disease (AD) is a progressive neurological disorder that causes brain cell degeneration and leads to dementia. Early and accurate detection of AD is crucial, as it allows timely treatment before the brain suffers permanent damage. In rece...

Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.

Scientific reports
Multiple Cervical Length (CL) measurements are typically acquired throughout the course of twin pregnancy to detect the early stages of labour and identify pregnancies at a high risk of preterm delivery. This study uses Machine-Learning (ML) approach...

Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD.

Scientific reports
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death in COPD patients. Machine learning (ML) approach is powerful but has a "black box" issue with an undirect interpretation of the ML te...