Machine learning and data mining: strategies for hypothesis generation.
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| Title: | Machine learning and data mining: strategies for hypothesis generation. |
|---|---|
| Authors: | Oquendo, M A, Baca-Garcia, E, Artés-Rodríguez, A, Perez-Cruz, F, Galfalvy, H C, Blasco-Fontecilla, H, Madigan, D, Duan, N |
| Source: | Molecular Psychiatry. Oct2012, Vol. 17 Issue 10, p956-959. 4p. 1 Graph. |
| Subjects: | Pathological physiology, Molecular biology, Machine learning, Data mining, Hypothesis |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Molecular Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 80374767 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning and data mining: strategies for hypothesis generation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Oquendo%2C+M+A%22">Oquendo, M A</searchLink><br /><searchLink fieldCode="AR" term="%22Baca-Garcia%2C+E%22">Baca-Garcia, E</searchLink><br /><searchLink fieldCode="AR" term="%22Artés-Rodríguez%2C+A%22">Artés-Rodríguez, A</searchLink><br /><searchLink fieldCode="AR" term="%22Perez-Cruz%2C+F%22">Perez-Cruz, F</searchLink><br /><searchLink fieldCode="AR" term="%22Galfalvy%2C+H+C%22">Galfalvy, H C</searchLink><br /><searchLink fieldCode="AR" term="%22Blasco-Fontecilla%2C+H%22">Blasco-Fontecilla, H</searchLink><br /><searchLink fieldCode="AR" term="%22Madigan%2C+D%22">Madigan, D</searchLink><br /><searchLink fieldCode="AR" term="%22Duan%2C+N%22">Duan, N</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Molecular+Psychiatry%22">Molecular Psychiatry</searchLink>. Oct2012, Vol. 17 Issue 10, p956-959. 4p. 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Pathological+physiology%22">Pathological physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+biology%22">Molecular biology</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Hypothesis%22">Hypothesis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Molecular Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/mp.2011.173 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 956 Subjects: – SubjectFull: Pathological physiology Type: general – SubjectFull: Molecular biology Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Data mining Type: general – SubjectFull: Hypothesis Type: general Titles: – TitleFull: Machine learning and data mining: strategies for hypothesis generation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Oquendo, M A – PersonEntity: Name: NameFull: Baca-Garcia, E – PersonEntity: Name: NameFull: Artés-Rodríguez, A – PersonEntity: Name: NameFull: Perez-Cruz, F – PersonEntity: Name: NameFull: Galfalvy, H C – PersonEntity: Name: NameFull: Blasco-Fontecilla, H – PersonEntity: Name: NameFull: Madigan, D – PersonEntity: Name: NameFull: Duan, N IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 13594184 Numbering: – Type: volume Value: 17 – Type: issue Value: 10 Titles: – TitleFull: Molecular Psychiatry Type: main |
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