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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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• Reference Manager
• EndNote
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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

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Unhealthy food intake and insufficient physical activities are related to obesity or lifestyle diseases, which can cause cardiovascular diseases, ultimately leading to death. However, many people are not aware of the importance of these factors, especially before cardiovascular development, although there are several good food habits that can be adopted. After the development of obesity or lifestyle diseases, nutrition and exercise control with appropriate medical therapies are required. Still, many patients do not recognize the importance of these habits. After cardiovascular disease development, nutrition and exercise with optimal medical and/or interventional therapies are required. However, some patients are not able to control their food intake and physical activities. At the advanced stage of heart failure, many things are restricted, including food intake and quality of life issues. At the end of life, nutritional care should be discussed. This book, Nutrition, Exercise, and End-of-Life Discussion in the Cardiovascular Field, addresses the importance of nutrition control before and after cardiovascular disease development, which consists of 14 peer-reviewed papers that cover the general population and patients with end-stage cardiovascular diseases.

Medicine --- Pharmacology --- milk intake --- mortality --- stroke --- Bayesian survival analysis --- time-to-event data --- JACC study --- unhealthy eating habits --- accumulation --- obesity --- central obesity --- general Japanese population --- atherosclerotic cardiovascular disease --- polyunsaturated fatty acids --- eicosapentaenoic acid --- docosahexaenoic acid --- arachidonic acid --- descriptive study --- heart failure --- vitamin D --- big data --- heart failure with preserved ejection fraction --- geriatric nutritional risk index --- behavioral modification stages --- nutrition counseling --- patient education --- acute coronary syndrome --- healthy behaviors --- diet --- legumes --- fish --- red/processed meat --- physical activity --- anxiety --- depression --- season --- calorie intake --- hospitalization --- malnutrition --- D-dimer --- intervention --- elderly --- cardiovascular mortality --- selenium --- coenzyme Q10 --- acylcarnitine --- brain natriuretic peptide --- cardiac function --- cardiomyopathy --- carnitine deficiency --- CPT2 --- end-stage kidney disease --- free fatty acid --- hemodialysis --- continuous glucose monitoring --- glucose fluctuation --- intracranial artery stenosis --- mean amplitude of glycemic excursions --- standard deviation --- palliative care --- end-of-life care discussion --- advance care planning --- food intake --- artificial nutrition --- onco-cardiology --- nutrition status --- cancer --- acute myocardial infarction --- plant-based dietary patterns --- grains --- nuts --- fruits --- vegetables --- cardiovascular disease --- milk intake --- mortality --- stroke --- Bayesian survival analysis --- time-to-event data --- JACC study --- unhealthy eating habits --- accumulation --- obesity --- central obesity --- general Japanese population --- atherosclerotic cardiovascular disease --- polyunsaturated fatty acids --- eicosapentaenoic acid --- docosahexaenoic acid --- arachidonic acid --- descriptive study --- heart failure --- vitamin D --- big data --- heart failure with preserved ejection fraction --- geriatric nutritional risk index --- behavioral modification stages --- nutrition counseling --- patient education --- acute coronary syndrome --- healthy behaviors --- diet --- legumes --- fish --- red/processed meat --- physical activity --- anxiety --- depression --- season --- calorie intake --- hospitalization --- malnutrition --- D-dimer --- intervention --- elderly --- cardiovascular mortality --- selenium --- coenzyme Q10 --- acylcarnitine --- brain natriuretic peptide --- cardiac function --- cardiomyopathy --- carnitine deficiency --- CPT2 --- end-stage kidney disease --- free fatty acid --- hemodialysis --- continuous glucose monitoring --- glucose fluctuation --- intracranial artery stenosis --- mean amplitude of glycemic excursions --- standard deviation --- palliative care --- end-of-life care discussion --- advance care planning --- food intake --- artificial nutrition --- onco-cardiology --- nutrition status --- cancer --- acute myocardial infarction --- plant-based dietary patterns --- grains --- nuts --- fruits --- vegetables --- cardiovascular disease