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Computational PsychiatryNew Perspectives on Mental Illness$
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A. David Redish and Joshua A. Gordon

Print publication date: 2016

Print ISBN-13: 9780262035422

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262035422.001.0001

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Call for Pragmatic Computational Psychiatry

Call for Pragmatic Computational Psychiatry

Integrating Computational Approaches and Risk-Prediction Models and Disposing of Causality

(p.259) 14 Call for Pragmatic Computational Psychiatry
Computational Psychiatry

Martin P. Paulus

Crane Huang

Katia M. Harlé

The MIT Press

Biological psychiatry is at an impasse. Despite several decades of intense research, few, if any, biological parameters have contributed to a significant improvement in the life of a psychiatric patient. It is argued that this impasse may be a consequence of an obsessive focus on mechanisms. Alternatively, a risk prediction framework provides a more pragmatic approach, because it aims to develop tests and measures which generate clinically useful information. Computational approaches may have an important role to play here. This chapter presents an example of a risk-prediction framework, which shows that computational approaches provide a significant predictive advantage. Future directions and challenges are highlighted.

Keywords:   Strüngmann Forum Report, computational psychiatry, risk-prediction framework, computational perspective, prediction models

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