Document Detail


Machine learning and data mining: strategies for hypothesis generation.
MedLine Citation:
PMID:  22230882     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.Molecular Psychiatry advance online publication, 10 January 2012; doi:10.1038/mp.2011.173.
Authors:
M A Oquendo; E Baca-Garcia; A Artés-Rodríguez; F Perez-Cruz; H C Galfalvy; H Blasco-Fontecilla; D Madigan; N Duan
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-1-10
Journal Detail:
Title:  Molecular psychiatry     Volume:  -     ISSN:  1476-5578     ISO Abbreviation:  -     Publication Date:  2012 Jan 
Date Detail:
Created Date:  2012-1-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9607835     Medline TA:  Mol Psychiatry     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Psychiatry, New York State Psychiatric Institute and Columbia University, New York, NY, USA.
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