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Efficacy analysis in clinical trials...
~
Cleophas, Ton J.
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Efficacy analysis in clinical trials an update = efficacy analysis in an era of machine learning /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Efficacy analysis in clinical trials an update/ by Ton J. Cleophas, Aeilko H. Zwinderman.
Reminder of title:
efficacy analysis in an era of machine learning /
Author:
Cleophas, Ton J.
other author:
Zwinderman, Aeilko H.
Published:
Cham :Springer International Publishing : : 2019.,
Description:
xi, 304 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:
Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.
Contained By:
Springer Nature eBook
Subject:
Clinical trials. -
Online resource:
https://doi.org/10.1007/978-3-030-19918-0
ISBN:
9783030199180
Efficacy analysis in clinical trials an update = efficacy analysis in an era of machine learning /
Cleophas, Ton J.
Efficacy analysis in clinical trials an update
efficacy analysis in an era of machine learning /[electronic resource] :by Ton J. Cleophas, Aeilko H. Zwinderman. - Cham :Springer International Publishing :2019. - xi, 304 p. :ill. (some col.), digital ;24 cm.
Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.
Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do.
ISBN: 9783030199180
Standard No.: 10.1007/978-3-030-19918-0doiSubjects--Topical Terms:
724498
Clinical trials.
LC Class. No.: R853.C55 / C546 2019
Dewey Class. No.: 615.50724
Efficacy analysis in clinical trials an update = efficacy analysis in an era of machine learning /
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by Ton J. Cleophas, Aeilko H. Zwinderman.
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Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.
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Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do.
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based on 0 review(s)
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EB R853.C55 C546 2019
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