Health and Public Health Applications for Decision Support Using Machine Learning

• Eará dahkkit: Rodrigues, Pedro Miguel (Doaimmaheaddji), Lobo Marques, João Alexandre (Doaimmaheaddji), Madeiro, João Paulo do Vale (Doaimmaheaddji)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: MDPI - Multidisciplinary Digital Publishing Institute 2023
• Fáttát: adult-onset dementia; Alzheimer's disease; magnetic resonance imaging; artificial intelligence; machine learning; neural networks; atherosclerosis; Doppler ultrasound; internal carotid artery; hemodynamic modeling; stroke; stress; emotion; action units; speech; audio visual; RNN-LSTM; petri-plates; colonies; machine-learning models; discrimination; Measurement uncertainty; Monte Carlo method; ECG; Cardiac health; COVID-19; signal processing; image processing; computerized diagnostic systems; subclinical renal damage; risk assessment tool; group-based trajectory modeling; screening strategy; CVD classification; data selection; convolutional neural network; pretrained model; deep learning; transfer learning; infected lung segmentation; quantification of lung disease severity; comparison between manual and automated image segmentation; deep neural network; COVID-19 detection; COVID-19 severity assessment; gait; neuromuscular control; movement synergy; overground walking; principal component analysis (PCA); largest Lyapunov exponent (LyE); time-series forecasting; blood glucose; diabetes; ensemble learning; artificial neural network; DDI (drug-drug interaction); CPR (chemical-protein relation); transformer; self-attention; GAT (graph-attention network); relation extraction; ChemProt; T5 (text-to-text transfer transformer); n/a
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication

"Health and Public Health Applications for Decision Support Using Machine Learning" is a reprint that explores the intersection of machine learning and health sciences. It presents a collection of research and innovations showcasing how data-driven algorithms can transform patient care, di...