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A roadmap for the development of applied computational psychiatry


Paulus, Martin P; Huys, Quentin J M; Maia, Tiago V (2016). A roadmap for the development of applied computational psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(5):386-392.

Abstract

Computational psychiatry (CP) is a burgeoning field that uses mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. CP has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Examples of CP are highlighted, and a phase-based pipeline for the development of clinical computational–psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. We propose that each phase has unique endpoints and deliverables that will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Application of computational approaches should be tested on healthy volunteers in phase I, transitioned to target populations in phases IB and IIA, and thoroughly evaluated using randomized clinical trials in phases IIB and III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make CP relevant for the clinic.

Abstract

Computational psychiatry (CP) is a burgeoning field that uses mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. CP has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Examples of CP are highlighted, and a phase-based pipeline for the development of clinical computational–psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. We propose that each phase has unique endpoints and deliverables that will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Application of computational approaches should be tested on healthy volunteers in phase I, transitioned to target populations in phases IB and IIA, and thoroughly evaluated using randomized clinical trials in phases IIB and III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make CP relevant for the clinic.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Uncontrolled Keywords:Biomarkers; Computational psychiatry; Development pipelines; Machine learning; Prediction; Translational psychiatry
Language:English
Date:2016
Deposited On:13 Sep 2016 15:00
Last Modified:30 Jan 2017 08:42
Publisher:Elsevier
ISSN:2451-9022
Publisher DOI:https://doi.org/10.1016/j.bpsc.2016.05.001

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