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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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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