LEADER 00868nam a2200253 i 4500 001 991002597839707536 005 20020503171113.0 008 010104s1940 it ||| | ita 035 $ab10387778-39ule_inst 035 $aEXGIL106686$9ExL 040 $aBiblioteca Interfacoltà$bita 082 0 $a580 100 1 $aStefanelli, Augusto$0359911 245 10$aElementi di biologia animale e vegetale /$cAugusto Stefanelli 260 $aBari :$bMacri,$c1940 300 $a639 p. ;$c23 cm. 650 4$aBotanica 650 4$aZoologia 907 $a.b10387778$b02-04-14$c27-06-02 912 $a991002597839707536 945 $aLE002 Scien. I F 25$g1$i2002000491992$lle002$o-$pE0.00$q-$rl$s- $t0$u1$v0$w1$x0$y.i10452783$z27-06-02 996 $aElementi di biologia animale e vegetale$9220028 997 $aUNISALENTO 998 $ale002$b01-01-01$cm$da $e-$fita$git $h0$i1 LEADER 01650oam 2200481 450 001 9910706188803321 005 20170818103029.0 035 $a(CKB)5470000002454535 035 $a(OCoLC)965702250$z(OCoLC)891598606 035 $a(OCoLC)995470000002454535 035 $a(EXLCZ)995470000002454535 100 $a20161209d1963 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA preliminary study of sediment transport parameters, Rio Puerco near Bernardo, New Mexico / by Carl F. Nordin, Jr 210 1$aWashington :$cUnited States Department of the Interior, Geological Survey,$d1963. 215 $a1 online resource (iv, C21 pages) $cillustrations, map 225 1 $aSediment transport in alluvial channels 225 1 $aGeological Survey professional paper ;$v462-C 320 $aIncludes bibliographical references (page C21). 606 $aSediment transport$zPuerco River (N.M. and Ariz.) 606 $aFluid mechanics 606 $aFluid mechanics$2fast 606 $aSediment transport$2fast 615 0$aSediment transport 615 0$aFluid mechanics. 615 7$aFluid mechanics. 615 7$aSediment transport. 700 $aNordin$b Carl F.$01394110 712 02$aGeological Survey (U.S.), 801 0$bOCLCE 801 1$bOCLCE 801 2$bCOP 801 2$bOCLCF 801 2$bOCLCQ 801 2$bGPO 906 $aBOOK 912 $a9910706188803321 996 $aA preliminary study of sediment transport parameters, Rio Puerco near Bernardo, New Mexico$93469348 997 $aUNINA LEADER 03856nam 2200481 450 001 9910813381703321 005 20211213160221.0 010 $a1-78728-322-4 035 $a(CKB)4100000005599755 035 $a(Au-PeEL)EBL5485013 035 $a(OCoLC)1048791966 035 $a(CaSebORM)9781787286702 035 $a(MiAaPQ)EBC5485013 035 $a(PPN)230109306 035 $a(EXLCZ)994100000005599755 100 $a20180917d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHealthcare analytics made simple $etechniques in healthcare computing using machine learning and Python /$fVikas Kumar 205 $a1st edition 210 1$aBirmingham, England :$cPackt,$d2018. 215 $a1 online resource (258 pages) 311 $a1-78728-670-3 320 $aIncludes bibliographical references and index. 330 $aAdd a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare. 606 $aMedical care$xData processing 606 $aMachine learning 606 $aPython (Computer program language) 615 0$aMedical care$xData processing. 615 0$aMachine learning. 615 0$aPython (Computer program language) 676 $a610.285 700 $aKumar$b Vikas$0846257 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813381703321 996 $aHealthcare analytics made simple$94000532 997 $aUNINA