IEEE

DGHME:A Hierarchical Hybrid Expert Multi-task Learning Model for Disease Grouping in Diabetes Complication Prediction

Abstract: Accurate identification and prediction of diabetes complications contribute to improved patient health. However, existing prediction models predominantly employ single-task learning (STL) ...

UC San Diego study uses machine learning to dive deeper into diabetes risk

A new tool named T1GRS enables researchers to get more accurate, further-reaching risk scores for the greater population ...
Medical Device and Diagnostic Industry (MD+DI)

How Large Language Models Are Reshaping Health Prediction & Clinical Decision Making

Large Language Models (LLMs) such as GPT-4, Gemini-Pro, Llama 2, and medical-domain-tuned variants like Med-PaLM 2 have ...
IEEE

Adaptive Random Fourier Features Fractional-order Maximum Versoria Criterion Algorithm for Active Noise Control

Abstract: Random Fourier Features (RFF) are widely employed in machine learning to approximate kernel functions with low-dimensional feature mappings, effectively reducing computational cost and ...