Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare

doi:10.3390/books978-3-0365-3741-2

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National Health Insurance Research Database; NHIRD; older adults; CABG; machine learning; overall survival prediction; feature selection; National Health Insurance Research Database; NHIRD; CABG; machine learning; medical expenditure predict; feature selection; Parkinson’s disease; neural network; deep learning; classification; post-analysis of data-driven models; rule design; multi-objective optimization; model performance; prediction of cardiovascular death; VR; virtual reality; VR e-sports; AI; immersive content; visual algorithm; visual system; online health care; game; multimorbidity; primary care; physical frailty; cognitive impairment; latent cluster analysis; sleep apnea; machine learning; deep learning; wearable systems; brain structure segmentation; fully convolutional networks; class imbalance; loss weighting; magnetic resonance images; ADNI; Alzheimer’s disease; mild cognitive impairment; MRI biomarkers; signal; T2 maps; texture; osteopenia; metabolic syndrome; socioeconomic status; lifestyle; machine learning; COVID-19; artificial intelligence; uses cases; European hospitals; benefits; facemask-wearing condition; transfer learning; convolutional neural network; deep learning; facial recognition; COVID-19; image segmentation; brain tumor; magnetic resonance imaging; multi-modality; COVID-19; Baidu index; random forest; bayesian optimization; LightGBM; medical records scoring; projection word embedding; long short-term memory; bidirectional encoder representations from transformers; artificial intelligence; natural language processing; electronic health records; patient satisfaction; service quality; SERVQUAL; Facebook; machine learning; patient online review; Malaysia; fall risk assessment; fall prediction; gait monitoring; sensor; electronic health records; machine learning; obstructive; polysomnography; prediction; sleep apnea; asthma; machine learning; prediction; risk factors; environment; prevention; pediatrics; artificial intelligence; machine learning; acute kidney injury; prediction model; food recognition; feature extraction; automatic diet monitoring; image analysis; volume estimation; interactive segmentation; food datasets; unsupervised machine learning; clustering; cornea; corneal topography; interocular symmetry; corneal elevation; keratoconus; inertial measurement unit; kinematic data; kinetic data; musculoskeletal disorders; neck movements; neck postures; OpenSim; random forest; mantle cell lymphoma; gene expression; MCL35 assay; artificial intelligence; machine learning; deep learning; artificial neural network; multilayer perceptron; immuno-oncology; overall survival; Helicobacter pylori infection; colorectal polyp; teeth disease; precancerous lesions; non-invasive; risk stratifying tool; random forest; artificial intelligence; coronary artery disease; major adverse cardiovascular events; fractional flow reserve; cardiac computed tomography; depression; social media; sentiment analysis; cardiovascular diseases; mHealth; self-management; usability score artificial intelligence; medical information systems; image matching; machine learning algorithms; morphological operations; COVID-19; enhanced MobileNetV2; IoT device sensor fusion; suspect detection and s tracking; face mask detection; remote monitoring

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Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare

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April 2022

608 pages

ISBN 978-3-0365-3742-9 (Hardback)
ISBN 978-3-0365-3741-2 (PDF)

https://doi.org/10.3390/books978-3-0365-3741-2

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This is a Reprint of the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare that was published in

Biology & Life Sciences

Summary

This book is a compilation of thirty research articles related to the current trends and techniques in medical informatics and healthcare with the application of Artificial Intelligence (AI) and Machine Learning (ML) to analyze both medical images and Electronic Health Records (EHR) in different domains to develop effective decision support systems as diagnostic aids and screening tools.

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