LEADER 03561 am 22006493u 450 001 9910337520303321 005 20230125202600.0 010 $a3-319-99713-0 024 7 $a10.1007/978-3-319-99713-1 035 $a(CKB)4100000007279067 035 $a(DE-He213)978-3-319-99713-1 035 $a(MiAaPQ)EBC5625481 035 $a(Au-PeEL)EBL5625481 035 $a(CaPaEBR)ebr11642142 035 $a(OCoLC)1108515312 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/28232 035 $a(PPN)232966494 035 $a(EXLCZ)994100000007279067 100 $a20181221d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFundamentals of Clinical Data Science$b[electronic resource] /$fedited by Pieter Kubben, Michel Dumontier, Andre Dekker 205 $a1st ed. 2019. 210 $aCham$cSpringer Nature$d2019 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (VIII, 219 p. 45 illus., 35 illus. in color.) 311 $a3-319-99712-2 327 $aData sources -- Data 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). 330 $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. 606 $aHealth informatics 606 $aBioinformatics 606 $aData Science 606 $aMedical Informatics 606 $aPrecision Medicine$xmethods 610 $aMedicine 610 $aHealth informatics 610 $aBioinformatics 615 0$aHealth informatics. 615 0$aBioinformatics. 615 12$aData Science. 615 12$aMedical Informatics. 615 22$aPrecision Medicine$xmethods. 676 $a502.85 700 $aKubben$b Pieter$4edt$01355554 702 $aKubben$b Pieter$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDumontier$b Michel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDekker$b Andre$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337520303321 996 $aFundamentals of Clinical Data Science$93359684 997 $aUNINA