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Signal Detection for Medical Scientists: Likelihood Ratio Based Test-Based Methodology presents the data mining techniques with focus on likelihood ratio test (LRT) based methods for signal detection. It emphasizes computational aspect of LRT methodology and is pertinent for first-time researchers and graduate students venturing into this interesting field.
The book is written as a reference book for professionals in pharmaceutical industry, manufactures of medical devices, and regulatory agencies. The book deals with the signal detection in drug/device evaluation, which is important in the post-market evaluation of medical products, and in the pre-market signal detection during clinical trials for monitoring procedures.
It should also appeal to academic researchers, and faculty members in mathematics, statistics, biostatistics, data science, pharmacology, engineering, epidemiology, and public health. Therefore, this book is well suited for both research and teaching.
Key Features:
Includes a balanced discussion of art of data structure, issues in signal detection, statistical methods and analytics, and implementation of the methods Provides a comprehensive summary of the LRT methods for signal detection including the basic theory and extensions for varying datasets that may be large post-market data or pre-market clinical trial data Contains details of scientific background, statistical methods, and associated algorithms that a reader can quickly master the materials and apply methods in the book on one’s own problems
849 kr
Läs direkt efter köp
Signal Detection for Medical Scientists: Likelihood Ratio Based Test-Based Methodology presents the data mining techniques with focus on likelihood ratio test (LRT) based methods for signal detection. It emphasizes computational aspect of LRT methodology and is pertinent for first-time researchers and graduate students venturing into this interesting field.
The book is written as a reference book for professionals in pharmaceutical industry, manufactures of medical devices, and regulatory agencies. The book deals with the signal detection in drug/device evaluation, which is important in the post-market evaluation of medical products, and in the pre-market signal detection during clinical trials for monitoring procedures.
It should also appeal to academic researchers, and faculty members in mathematics, statistics, biostatistics, data science, pharmacology, engineering, epidemiology, and public health. Therefore, this book is well suited for both research and teaching.
Key Features:
Includes a balanced discussion of art of data structure, issues in signal detection, statistical methods and analytics, and implementation of the methods Provides a comprehensive summary of the LRT methods for signal detection including the basic theory and extensions for varying datasets that may be large post-market data or pre-market clinical trial data Contains details of scientific background, statistical methods, and associated algorithms that a reader can quickly master the materials and apply methods in the book on one’s own problems
719 kr
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603 kr
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1 235 kr
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900 kr
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1 111 kr
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This two-volume set, CCIS 2274 and CCIS 2275, constitutes the refereed proceedings of the 39th National Conference on China Computer Federation, CCF NCCA 2024, held in Harbin, China, during July 15–18, 2024.
The 48 full papers presented here were carefully reviewed and selected from 238 submissions. These papers are organized in the following topical sections:
Part I: Artificial Intelligence and Applications; Data Science and Technology.
Part II: Pattern Recognition & Machine Learning; Network Communication and Security; Frontier and Comprehensive Applications; Data Science and Technology.
900 kr
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1 141 kr
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This two-volume set, CCIS 2274 and CCIS 2275, constitutes the refereed proceedings of the 39th National Conference on China Computer Federation, CCF NCCA 2024, held in Harbin, China, during July 15–18, 2024.
The 48 full papers presented here were carefully reviewed and selected from 238 submissions. These papers are organized in the following topical sections:
Part I: Artificial Intelligence and Applications; Data Science and Technology.
Part II: Pattern Recognition & Machine Learning; Network Communication and Security; Frontier and Comprehensive Applications; Data Science and Technology.