Barron, DS;
Baker, JT;
Budde, KS;
Bzdok, D;
Eickhoff, SB;
Friston, KJ;
Fox, PT;
... Krystal, JH; + view all
(2021)
Decision Models and Technology Can Help Psychiatry Develop Biomarkers.
Frontiers in Psychiatry
, 12
, Article 706655. 10.3389/fpsyt.2021.706655.
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Abstract
Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.
| Type: | Article |
|---|---|
| Title: | Decision Models and Technology Can Help Psychiatry Develop Biomarkers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fpsyt.2021.706655 |
| Publisher version: | https://doi.org/10.3389/fpsyt.2021.706655 |
| Language: | English |
| Additional information: | Copyright © 2021 Barron, Baker, Budde, Bzdok, Eickhoff, Friston, Fox, Geha, Heisig, Holmes, Onnela, Powers, Silbersweig and Krystal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | psychiatry, biomarker, digital phenotype, diagnosis, Bayesian inference, decision model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10135889 |
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