Special Issue on Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools.

This document is a summary of a special issue of the journal Applied Sciences on real-time diagnosis algorithms in biomedical applications and decision support tools. The issue includes ten papers that showcase advances in data analysis techniques and their potential to improve doctors' daily tasks. Five papers focus on real-time diagnosis algorithms, covering topics such as ventricular fibrillation detection, bio-impedance spectrometry, epilepsy localization, physical activity recognition, and exhaled acetone detection. The other five papers explore decision support tools, including machine learning models for disease classification, optical breast contour detection, analysis of human body balance, handwriting analysis for neurodegenerative disease assessment, and automatic detection of brain markers using deep learning. The issue concludes that these research topics have a high social impact and contribute to improving human lives. [Extracted from the article]