Non-communicable Diseases, Big Data and Artificial Intelligence

doi:10.3390/books978-3-0365-4847-0

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Keywords

artificial intelligence; computer-aided diagnosis; facial phenotypes; machine learning; complexity theory; machine learning; dementia; cognitive dysfunction; neuropsychological tests; mental status and dementia tests; spontaneous intracerebral hemorrhage (SICH); machine learning; 90-day function outcome; mortality; osteoarthritis; venous thrombosis; VTE risk prediction; machine learning algorithm; population-based cohort study; pituitary adenoma; craniopharyngioma; optic chiasm; multicenter; treatment outcome; liver neoplasms; deep learning; diabetic complication; gene-gene interaction; AGER; IL6R; multiple sclerosis; DNA methylation; entropy; artificial intelligence; atherosclerosis; plaque characterization; physical activity; osteoporosis; osteoporotic fracture; vertebral fracture; hip fracture; distal radius fracture; small for gestational age; exposure to radiation; machine learning; prediction; coronary plaque; machine learning; major adverse cardiovascular events; coronary artery disease; coronary computed tomographic angiography; acute pancreatitis; machine learning; predictor; interventions; mortality; type 2 diabetes mellitus (T2DM); machine learning; prediction model; Chinese elderly; machine learning; prediabetes; incident diabetes; predictive models

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Non-communicable Diseases, Big Data and Artificial Intelligence

Edited by

August 2022

214 pages

ISBN978-3-0365-4848-7 (Hardback)
ISBN978-3-0365-4847-0 (PDF)

https://doi.org/10.3390/books978-3-0365-4847-0

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This is a Reprint of the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence that was published in

Medicine & Pharmacology

Public Health & Healthcare

Summary

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine.