The amount of data medical databases doubles every 20 months, and
physicians are at a loss to analyze them. Also, traditional data
analysis has 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.
Consequently, proper data-based health decisions will soon be
impossible. Obviously, it is time that medical and health
professionals mastered their reluctance to use machine learning
methods and this was the main incentive for the authors to complete
a series of three textbooks entitled "Machine Learning in Medicine
Part One, Two and Three, Springer Heidelberg Germany, 2012-2013",
describing in a nonmathematical way over sixty machine learning
methodologies, as available in SPSS statistical software and other
major software programs. Although well received, it came to our
attention that physicians and students often lacked time to read
the entire books, and requested a small book, without background
information and theoretical discussions and highlighting technical
details. For this reason we produced a 100 page cookbook, entitled
"Machine Learning in Medicine - Cookbook One", with data examples
available at extras.springer.com for self-assessment and with
reference to the above textbooks for background information.
Already at the completion of this cookbook we came to realize, that
many essential methods were not covered. The current volume,
entitled "Machine Learning in Medicine - Cookbook Two" is
complementary to the first and also intended for providing a more
balanced view of the field and thus, 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. Similarly to
Machine Learning in Medicine - Cookbook One, the current work will
describe stepwise analyses of over twenty machine learning methods,
that are, likewise, based on the three major machine learning
methodologies: Cluster methodologies (Chaps. 1-3) Linear
methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In
extras.springer.com the data files of the examples are given, as
well as XML (Extended Mark up Language), SPS (Syntax) and ZIP
(compressed) files for outcome predictions in future patients. In
addition to condensed versions of the methods, fully described in
the above three textbooks, an introduction is given to SPSS Modeler
(SPSS' data mining workbench) in the Chaps. 15, 18, 19, while
improved statistical methods like various automated analyses and
Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We
should emphasize that all of the methods described have been
successfully applied in practice by the authors, both of them
professors in applied statistics and machine learning at the
European Community College of Pharmaceutical Medicine in Lyon
France. We recommend the current work not only as a training
companion to investigators and students, because of plenty of step
by step analyses given, but also as a brief introductory text to
jaded clinicians new to the methods. For the latter purpose,
background and theoretical information have been replaced with the
appropriate references to the above textbooks, while single
sections addressing "general purposes", "main scientific questions"
and "conclusions" are given in place. Finally, we will demonstrate
that modern machine learning performs sometimes better than
traditional statistics does. Machine learning may have little
options for adjusting confounding and interaction, but you can add
propensity scores and interaction variables to almost any machine
learning method.
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