Machine Learning, Statistical Learning and the Future of Biological Research in Psychiatry

TY - JOUR

T1 - Machine Learning, Statistical Learning and the Future of Biological Research in Psychiatry

AU - Iniesta, Raquel

AU - Stahl, Daniel Richard

AU - McGuffin, Peter

PY - 2016/9

Y1 - 2016/9

N2 - Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous vari- ables, 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 classifica- tion 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.

AB - Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous vari- ables, 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 classifica- tion 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.

UR - http://www.scopus.com/inward/record.url?scp=84978080375&partnerID=8YFLogxK

U2 - 10.1017/S0033291716001367

DO - 10.1017/S0033291716001367

M3 - Literature review

SN - 0033-2917

VL - 46

SP - 2455

EP - 2465

JO - Psychological medicine

JF - Psychological medicine

IS - 12

ER -