4.7 Review

Machine learning, statistical learning and the future of biological research in psychiatry

Journal

PSYCHOLOGICAL MEDICINE
Volume 46, Issue 12, Pages 2455-2465

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0033291716001367

Keywords

Machine learning; outcome prediction; personalized medicine; predictive modelling; statistical learning

Funding

  1. European Commission
  2. EC [LSHB-CT-2003-503428]
  3. Innovative Medicine Initiative Joint Undertaking (IMI-JU) [115008]
  4. European Union
  5. European Community [286213]
  6. King's College London

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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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