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An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.
Zacharias Voulgaris Data Science. Mindset, Methodologies, and Misconceptions zacharias voulgaris data science mindset methodologies and misconceptions
S. Finlay Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods zacharias voulgaris data science mindset methodologies and misconceptions
Zacharias Voulgaris, Yunus Emrah Bulut AI for Data Science. Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond zacharias voulgaris data science mindset methodologies and misconceptions
James Godfrey Methods for Data Science. I - Introductory Applied Mathematics zacharias voulgaris data science mindset methodologies and misconceptions
Jay Etchings A. Strategies in Biomedical Data Science. Driving Force for Innovation zacharias voulgaris data science mindset methodologies and misconceptions
Jeffrey Strickland Data Science and Analytics for Ordinary People zacharias voulgaris data science mindset methodologies and misconceptions
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Fabio Zander Doubting the efficacy of the growth mindset. A literature review zacharias voulgaris data science mindset methodologies and misconceptions
Gurkirat Kaur We Are Teaching, Are They Learning zacharias voulgaris data science mindset methodologies and misconceptions
Thomas Mailund Beginning Data Science in R. Data Analysis, Visualization, and Modelling for the Data Scientist zacharias voulgaris data science mindset methodologies and misconceptions
Frank Kane Hands-On Data Science and Python Machine Learning zacharias voulgaris data science mindset methodologies and misconceptions


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