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[null,"1.","Record Nr.",null,"UNINA9910409834903321"]
[null,null,"Autore",null,"Juhnke Christin"]
[null,null,"Titolo",null,"Adaptive Nutzenbewertung für Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen : Die Abwägung von Patientennutzen, Evidenz und Zugang / / Christin Juhnke, Axel C. Mühlbacher"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, : MWV Medizinisch Wissenschaftliche Verlagsgesellschaft, 2016"]
[{"$nulls":4},"[s.l.] : , : MWV Medizinisch Wissenschaftliche Verlagsgesellschaft, , 2016"]
[null,null,"Descrizione fisica",null,"1 online resource (1 p.)"]
[null,null,"Soggetti",null,"Medical / Administration"]
[{"$nulls":4},"Medicine"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Das GKV-VSG führte erstmalig die Nutzenbewertung von Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen und invasivem Charakter ein. Zukünftig wird die Entscheidung über die Erstattungsfähigkeit von der Dokumentation von Nutzen und Schaden abhängig sein. Grundsätzlich kann die Nutzenbewertung in drei Phasen unterteilt werden: Messen von kausalen Effekten einer Intervention, Bewertung der gemessenen Effekte und Entscheidung über die Erstattungsfähigkeit eines aggregierten Gesamtnutzens. Um den Anforderungen zu begegnen, stellen adaptive Studiendesigns, die multikriterielle Entscheidungsanalyse und die adaptive Nutzenbewertung ein zukunftsfähiges Konzept für einen schnellen Zugang von Patienten zu innovativen Behandlungsmethoden bei hoher Qualität und Sicherheit dar."]
[null,"2.","Record Nr.",null,"UNINA9910303442303321"]
[null,null,"Autore",null,"Chellappan Subhashini"]
[null,null,"Titolo",null,"Practical Apache Spark : Using the Scala API / / by Subhashini Chellappan, Dharanitharan Ganesan"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018"]
[null,null,"ISBN",null,"9781484236529"]
[{"$nulls":4},"1484236521"]
[null,null,"Edizione",null,"[1st ed. 2018.]"]
[null,null,"Descrizione fisica",null,"1 online resource (288 pages)"]
[null,null,"Disciplina",null,"005.114"]
[null,null,"Soggetti",null,"Big data"]
[{"$nulls":4},"Open source software"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Programming languages (Electronic computers)"]
[{"$nulls":4},"Big Data"]
[{"$nulls":4},"Open Source"]
[{"$nulls":4},"Programming Languages, Compilers, Interpreters"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Sommario/riassunto","Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage. You will: Discover the functional programming features of Scala Understand the complete architecture of Spark and its components Integrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib "]
[{"$nulls":4},"packages."]