Simon k poon (6 Ergebnisse)

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  • Sprache: Englisch

    Verlag: Springer, 2016

    3319346296 / 9783319346298

    • Softcover

    Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections

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    Zustand: Neu

    EUR 116,14

    EUR 10,91 Versand 
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    Zustand: New. In English.

  • Sprache: Englisch

    Verlag: Springer, 2014

    3319038001 / 9783319038001

    • Hardcover

    Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections

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    Zustand: Neu

    EUR 116,14

    EUR 13,14 Versand 
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    Zustand: New. In English.

  • Sprache: Englisch

    Verlag: Springer International Publishing, 2016

    3319346296 / 9783319346298

    • Softcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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    Zustand: Neu

    EUR 106,99

    EUR 62,00 Versand 
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    Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.

  • Sprache: Englisch

    Verlag: Springer International Publishing, 2014

    3319038001 / 9783319038001

    • Hardcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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    Zustand: Neu

    EUR 106,99

    EUR 62,80 Versand 
    Versand von Deutschland nach USA

    Anzahl: 1 verfügbar

    Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.

  • Sprache: Englisch

    Verlag: Springer-Verlag New York Inc, 2016

    3319346296 / 9783319346298

    • Softcover

    Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books

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    Zustand: Neu

    EUR 153,69

    EUR 34,92 Versand 
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    Paperback. Zustand: Brand New. reprint edition. 260 pages. 9.25x6.10x0.59 inches. In Stock.

  • Sprache: Englisch

    Verlag: Springer, 2014

    3319038001 / 9783319038001

    • Hardcover

    Anbieter: Buchpark, Trebbin, DeutschlandBuchpark

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    Zustand: Gebraucht - Sehr gut

    EUR 83,61

    EUR 105,00 Versand 
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    Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 260 | Sprache: Englisch | Produktart: Bücher | This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.