Titlebook: Snake Venoms; P. Gopalakrishnakone,Hidetoshi Inagaki,Carl-Wilhel Living reference work 20200th edition

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Rupamoni Thakur,Ashis K. Mukherjeeysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing..Obviously, it is time that medical and health professional

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Debashree Saikia,Ashis K. Mukherjee time-friendly.The book is multipurpose, (1) an introduction.The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple

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J. P. Chippaux,R. P. Stock,A. Massougbodjiysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing..Obviously, it is time that medical and health professional

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Fiorella Tonello,Michela Rigoniysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing..Obviously, it is time that medical and health professional

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Carl-Wilhelm Vogel,David C. Fritzingerction for the ignorant, (2) a primer to the inexperienced, (.Unique features of the book involve the following..1.This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medica

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Denise V. Tambourgi,Carmen W. van den Bergculty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.