The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Machine learning, statistical learning and the future of biological research in psychiatry
Open accessAbstract
Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.
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Background
Cited by
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The promise of machine learning in predicting treatment outcomes in psychiatry
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Etiology in psychiatry: embracing the reality of poly‐gene‐environmental causation of mental illness
Uher, R., & Zwicker, A. (2017) · World Psychiatry · doi:10.1002/wps.20436 · Cited by 331
Metadata
- Authors
- Iniesta, R., Stahl, D., & McGuffin, P.
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2016-07-13
- Volume
- 46
- Issue
- 12
- Pages
- 2455-2465
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W2472069995
- Cited by
- 328
- References
- 12
- FWCI
- 40.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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