Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...
Background: Manual analysis of ECG reports can be time-consuming and difficult to perform. Machine learning (ML) models have shown higher success when trained on raw time-series rather than images.
ABSTRACT: Malaria remains a major public health challenge in the Democratic Republic of Congo (DRC), particularly in Lubumbashi, where traditional diagnostic methods are struggling to meet growing ...
Credit: Getty Images Researchers developed a machine learning-based tool to predict individualized risk for recurrent multiple sclerosis activity after disease modifying therapy discontinuation. A ...
Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Background: Sleep apnea is a common sleep disorder associated with high ...
A digital innovation initiative about fault anomalies has been selected as one of the first projects for the new Microsoft AI Co-Innovation Lab. GREEN BAY, Wis. - June 27, 2025 - BW Converting is ...
Society tends to protect the identity of cadavers, so not much is known about No. 817. We know the 72-year-old woman was from Montana and that her final cause of death was cancer. One can infer she ...
Abstract: This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular ...
As an early childhood educator, I have often come across people who believe that early learners aren’t capable of making decisions or guiding their own learning. But that is a misconception—young ...
ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method ...
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