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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Machine Learning in Medicine - Cookbook Three (Paperback, 2014 ed.)
Loot Price: R1,469
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Machine Learning in Medicine - Cookbook Three (Paperback, 2014 ed.)
Series: SpringerBriefs in Statistics
Expected to ship within 10 - 15 working days
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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 medical and health care
community covering the field of machine learning have been
published to date. 2. Each chapter of the book can be studied
without the need to consult other chapters, and can, for the
readership's convenience, be downloaded from the internet.
Self-assessment examples are available at extras.springer.com. 3.
An adequate command of machine learning methodologies is a
requirement for physicians and other health workers, particularly
now, because the amount of medical computer data files currently
doubles every 20 months, and, because, soon, it will be impossible
for them to take proper data-based health decisions without the
help of machine learning. 4. Given the importance of knowledge of
machine learning in the medical and health care community, and the
current lack of knowledge of it, the readership will consist of any
physician and health worker. 5. The book was written in a simple
language in order to enhance readability not only for the advanced
but also for the novices. 6. The book is multipurpose, it is an
introduction for ignorant, a primer for the inexperienced, and a
self-assessment handbook for the advanced. 7. The book, was,
particularly, written for jaded physicians and any other health
care professionals lacking time to read the entire series of three
textbooks. 8. Like the other two cookbooks it contains technical
descriptions and self-assessment examples of 20 important computer
methodologies for medical data analysis, and it, largely, skips the
theoretical and mathematical background. 9. Information of
theoretical and mathematical background of the methods described
are displayed in a "notes" section at the end of each chapter.
10.Unlike traditional statistical methods, the machine learning
methodologies are able to analyze big data including thousands of
cases and hundreds of variables. 11. The medical and health care
community is little aware of the multidimensional nature of current
medical data files, and experimental clinical studies are not
helpful to that aim either, because these studies, usually, assume
that subgroup characteristics are unimportant, as long as the study
is randomized. This is, of course, untrue, because any subgroup
characteristic may be vital to an individual at risk. 12. To date,
except for a three volume introductary series on the subject
entitled "Machine Learning in Medicine Part One, Two, and Thee,
2013, Springer Heidelberg Germany" from the same authors, and the
current cookbook series, no books on machine learning in medicine
have been published. 13. Another unique feature of the cookbooks is
that it was jointly written by two authors from different
disciplines, one being a clinician/clinical pharmacologist, one
being a mathematician/biostatistician. 14. The authors have also
jointly been teaching at universities and institutions throughout
Europe and the USA for the past 20 years. 15. The authors have
managed to cover the field of medical data analysis in a
nonmathematical way for the benefit of medical and health workers.
16. The authors already successfully published many statistics
textbooks and self-assessment books, e.g., the 67 chapter textbook
entitled "Statistics Applied to Clinical Studies 5th Edition, 2012,
Springer Heidelberg Germany" with downloads of 62,826 copies. 17.
The current cookbook makes use, in addition to SPSS statistical
software, of various free calculators from the internet, as well as
the Konstanz Information Miner (Knime), a widely approved free
machine learning package, and the free Weka Data Mining package
from New Zealand. 18. The above software packages with hundreds of
nodes, the basic processing units including virtually all of the
statistical and data mining methods, can be used not only for data
analyses, but also for appropriate data storage. 19. The current
cookbook shows, particularly, for those with little affinity to
value tables, that data mining in the form of a visualization
process is very well feasible, and often more revealing than
traditional statistics. 20.The Knime and Weka data miners uses
widely available excel data files. 21. In current clinical research
prospective cohort studies are increasingly replacing the costly
controlled clinical trials, and modern machine learning
methodologies like probit and tobit regressions as well as neural
networks, Bayesian networks, and support vector machines prove to
better fit their analysis than traditional statistical methods do.
22. The current cookbook not only includes concise descriptions of
standard machine learning methods, but also of more recent methods
like the linear machine learning models using ordinal and loglinear
regression. 23. Machine learning tends to increasingly use
evolutionary operation methodologies. Also this subject has been
covered. 24. All of the methods described have been applied in the
authors' own research prior to this publication.
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