How real-world data can facilitate the development of precision medicine treatment in psychiatry

Koch, Elise, Pardinas, Antonio F. ORCID: https://orcid.org/0000-0001-6845-7590, O’Connell, Kevin S., Selvaggi, Pierluigi, Camacho Collados, Jose ORCID: https://orcid.org/0000-0003-1618-7239, Babic, Aleksandar, Marshall, Serena E., Van der Eycken, Erik, Angulo, Cecilia, Lu, Yi, Sullivan, Patrick F., Dale, Anders M., Molden, Espen, Posthuma, Danielle, White, Nathan, Schubert, Alexander, Djurovic, Srdjan, Heimer, Hakon, Stefánsson, Hreinn, Stefánsson, Kári, Werge, Thomas, Sønderby, Ida, O’Donovan, Michael C. ORCID: https://orcid.org/0000-0001-7073-2379, Walters, James T.R. ORCID: https://orcid.org/0000-0002-6980-4053, Milani, Lili and Andreassen, Ole A. 2024. How real-world data can facilitate the development of precision medicine treatment in psychiatry. Biological Psychiatry 10.1016/j.biopsych.2024.01.001

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Abstract

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification, and holds great potential in mental disorders. However, several important factors are needed to transform current practice into a “precision psychiatry” framework. Most important are (1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, (2) the development and validation of advanced analytical tools for stratification and prediction, and (3) the development of clinically useful management platforms for patient monitoring that can be integrated into healthcare systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements – well-powered samples from large biobanks, integrated with electronic health records and health registry data using novel artificial intelligence algorithms – to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We include use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Medicine Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2024-01-05
Publisher:	Elsevier
ISSN:	0006-3223
Date of First Compliant Deposit:	8 January 2024
Date of Acceptance:	2 January 2024
Last Modified:	18 Jan 2024 10:53
URI:	https://orca.cardiff.ac.uk/id/eprint/165351

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