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 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 professionals
mastered their reluctance to use machine learning and the current
100 page cookbook should be helpful to that aim. It covers in a
condensed form the subjects reviewed in the 750 page three volume
textbook by the same authors, entitled Machine Learning in Medicine
I-III (ed. by Springer, Heidelberg, Germany, 2013) and was written
as a hand-hold presentation and must-read publication. It was
written not only to investigators and students in the fields, but
also to jaded clinicians new to the methods and lacking time to
read the entire textbooks.
General purposes and scientific questions of the methods are
only briefly mentioned, but full attention is given to the
technical details. The two authors, a statistician and current
president of the International Association of Biostatistics and a
clinician and past-president of the American College of Angiology,
provide plenty of step-by-step analyses from their own research and
data files for self-assessment are available at
extras.springer.com.
From their experience the authors demonstrate that 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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