000			02311nam a2200229 a 4500
001			38908
003			0000000000
005			20240411192909.0
008			201117n s 000 0 eng d
010			_a2018963226
020			_a978-3-319-99712-4
245	1	0	_aFundamentals of clinical data science _h[electronic resource] / _cEdited by Pieter Kubben, Michel Dumontier, Andre Dekker.
260			_aCham, Switzerland : _bSpringerOpen, _c2019.
300			_a1 online resource.
500			_aIncluded bibliographical references and index.
505	0		_aData at scale -- Standards in healthcare data -- Using FAIR data / data stewardship -- Privacy / deidentification -- Preparing your data -- Creating a predictive model -- Diving deeper into models -- Validation and Evaluation of reported models -- Clinical decision support systems -- Mobile app development -- Operational excellence -- Value Based Healthcare (Regulatory concerns).
520			_aThis open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book's promise is "no math, no code" and will explain the topics in a style that is optimized for a healthcare audience.
650		7	_aData Science. _2sears
650		7	_aMedical informatics. _2sears
856			_uhttps://drive.google.com/file/d/1ToGmUNuvB2_usPyIpDLpttpKY3A43Kv0/view?usp=sharing
999			_c10085 _d10085