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Machine Learning in Medicine - a Complete Overview - Zwinderman, Aeilko H.

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        Présentation Machine Learning In Medicine - A Complete Overview de Zwinderman, Aeilko H. Format Broché

         - Livre Sciences de la vie et de la terre

        Livre Sciences de la vie et de la terre - Zwinderman, Aeilko H. - 30/09/2016 - Broché - Langue : Anglais

        . .

      • Auteur(s) : Zwinderman, Aeilko H. - Cleophas, Ton J.
      • Editeur : Springer International Publishing
      • Langue : Anglais
      • Parution : 30/09/2016
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 540
      • Expédition : 809
      • Dimensions : 23.5 x 15.5 x 2.9
      • ISBN : 3319386387



      • Résumé :
        The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters. The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations. So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies. Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.

        Biographie:
        The authors are well-qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015), and Professor Cleophas is past-president of the American College of Angiology (2000-2002).

        Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.
        Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings Pharmaceutical Medicine and Medicines Development.
        From their expertise theyshould be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
        The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are concerned, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis, and they do hope that the current production Regression Analysis for Starters and 2nd Levelers will be a helpful companion for the purpose.
        Five textbookscomplementary to the current production and written by the same authors are
        Statistics applied to clinical studies 5th edition, 2012,
        Machine learning in medicine a complete overview, 2015,
        SPSS for starters and 2nd levelers 2nd edition, 2015,
        Clinical data analysis on a pocket calculator 2nd edition, 2016,
        Modern Meta-analysis, 2017
        Regression Analysis in Medical Research, 2018
        all of them published by Springer
        ...

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