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Chest pain. --- Chest --- Thoracalgia --- Thoracic pain --- Thoracodynia --- Pain --- Diseases

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Chest pain. --- Coronary heart disease. --- Coronary arteries --- Coronary arteriosclerosis --- Coronary disease --- Coronary thrombosis --- Ischemic heart disease --- Myocardial ischemia --- Heart --- Type A behavior --- Chest --- Thoracalgia --- Thoracic pain --- Thoracodynia --- Pain --- Diseases

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The condition known as “chest pain with normal coronary arteries” or “cardiac syndrome X” has puzzled physicians since the advent of coronary arteriography. Although epicardial coronary artery spasm, as seen in Prinzmetal’s variant angina, explains a proportion of cases of typical chest pain despite normal coronary arteriograms, many patients who seek medical attention for exertional and rest angina in the absence of obstructive coronary artery disease are not variant angina cases. This syndrome therefore constitutes both a diagnostic and therapeutic challenge. Chest Pain with Normal Coronary Arteries has been written by many of the most active international research groups and comprehensively tackles the clinical presentation and the pathogenesis of the condition, as well as its management. Abnormalities of the coronary microcirculation have remained elusive to conventional imaging and researchers appear only recently to be making progress in obtaining much needed information in this field. The functional aspects of the coronary microcirculation, its clinical presentation and prognosis, as well as the diagnostic tests used for the assessment of microvascular dysfunction are important topics highlighted in this book, which also includes useful clinical diagnostic algorithms, thus bringing this subject closer to the practicing cardiologist. This book thus represents a practical tool for the clinician and a bank of information and new ideas for research scientists and clinical researchers interested in understanding the causes and mechanisms of the condition.

Chest -- Surgery. --- Chest pain. --- Medicine. --- Angina pectoris --- Chest pain --- Coronary arteries --- Syndromes --- Angina Pectoris --- Myocardial Ischemia --- Heart Diseases --- Cardiovascular Diseases --- Diseases --- Microvascular Angina --- Coronary arteries. --- Chest --- Thoracalgia --- Thoracic pain --- Thoracodynia --- Pain --- Medicine & Public Health. --- Medicine/Public Health, general. --- Arteries --- Heart --- Blood-vessels --- Clinical sciences --- Medical profession --- Human biology --- Life sciences --- Medical sciences --- Pathology --- Physicians --- Health Workforce

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Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

Medicine --- Pharmacology --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification --- n/a --- naïve Bayes classifiers

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Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

Medicine --- Pharmacology --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification