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Psychotherapy Research: Special Issue Call for Papers

Machine Learning in psychotherapy

Abstract deadline
31 December 2019

About this Special Issue

Background and aim:

Machine Learning (ML) refers to a broad set of advanced statistical techniques that enable us to discover patterns in data and to make predictions about natural phenomena and human activity. We now live in a time of exponential growth in information about nature, our societies and our health, affording us unprecedented opportunities to learn from data. Leveraging the potential of "big data" in today's highly inter-connected world, ML has led to important breakthroughs in the fields of artificial intelligence, internet applications, genetics and medicine. In recent years, psychotherapy researchers have begun to apply ML as a tool to understand psychopathology and to enhance the precision of psychological assessment and treatment. The aim of this special issue is to offer psychotherapy researchers a primer on ML concepts, applications, and methodological pointers to interpret and appraise the rigor of this emerging approach.

Scope for contributions:

Empirical or methodological studies that apply ML approaches using psychotherapy data. This might include the use of ML techniques for: variable selection, discovery of clinically relevant phenotypes, pattern recognition in process and/or outcomes data, development of prediction and classification algorithms, development of clinical decision-rules.

Submission process:

  • Submit your paper online at https://mc.manuscriptcentral.com/tpsr
  • Address your cover letter to the guest editor: J Delgadillo, University of Sheffield
  • Deadline for submissions to this special issue: 31 December 2019
  • Submissions will be screened according to the scope for contributions criteria. Papers that fit the scope will be peer reviewed by at least 2 reviewers.
  • Up to 5 accepted papers will be included in the special section.