Despite extensive research on AI's theoretical benefits in entrepreneurship, few studies compare machine learning models' effectiveness using real-world data or address challenges like model interpretability and overfitting. This study investigates h...
This study investigated dynamic brain network changes and their genetic correlations in children with idiopathic generalized epilepsy (IGE). We included 26 children with IGE and 35 healthy controls, all participants underwent resting-state functional...
Rapid and accurate diagnosis of emerging inflammatory illnesses is challenging due to overlapping clinical features with existing conditions. We demonstrate an approach that integrates proteomic analysis with machine learning to identify diagnostic p...
BACKGROUND: Despite the high prevalence and significant burden of depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for depression assessment. However, a comprehensive quantit...
BACKGROUND: Diabetic nephropathy (DN), a severe complication of diabetes, is characterized by proteinuria, hypertension, and progressive renal function decline, potentially leading to end-stage renal disease. The International Diabetes Federation pro...
PURPOSE: Cyclic alternating patterns (CAP) of sleep can be observed through electroencephalogram (EEG) signals. Analyzing CAP can provide valuable insights into different abnormalities relating to sleep. CAP comprises of two phases: A and B, characte...
Global interpretability in machine learning holds great potential for extracting meaningful insights from neuroimaging data to improve our understanding of brain function. Although various approaches exist to identify key contributing features at bot...
A portable electrochemical aptasensor integrated with machine learning was developed for rapid and on-site detection of Staphylococcus aureus (S. aureus) in food and beverage samples. The aptasensor was fabricated using screen-printed carbon electrod...
OBJECTIVES: Accurately predicting the mortality risk of hypothermia patients is crucial for clinical decision-making, offering ample time for physicians to intervene. However, existing methods are invasive and difficult to implement in pre-hospital s...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.