000			02684nam a22003135i 4500
001			65822
005			20240104063428.0
010			_a978-3-319-04181-0 _dcompra
090			_a65822
100			_a20150401d2014 k||y0pory50 ba
101			_aeng
102			_aDE
200			_aMachine learning in medicine _bDocumento electrónico _ecookbook _fTon J. Cleophas, Aeilko H. Zwinderman
210			_aCham _cSpringer International Publishing _d2014
215			_aXI, 137 p. _cil.
225			_aSpringerBriefs in Statistics
300			_aColocação: Online
303			_aThe 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.
606			_aMedicina _xProcessamento de dados
606			_aMachine learning
680			_aR858
700			_aCleophas _bTon J.
701			_932253 _aZwinderman _bAeilko H. _4070
801			_aPT _gRPC
856			_uhttp://dx.doi.org/10.1007/978-3-319-04181-0
942			_2lcc _cF _n0