Understanding as a bottleneck for the data-driven approach to psychiatric science

Title data

Crook, Barnaby:
Understanding as a bottleneck for the data-driven approach to psychiatric science.
In: Philosophy and the Mind Sciences. Vol. 4 (2023) .
ISSN 2699-0369
DOI: https://doi.org/10.33735/phimisci.2023.9658

Official URL:

Abstract in another language

The data-driven approach to psychiatric science leverages large volumes of patient data to construct machine learning models with the goal of optimizing clinical decision making. Advocates claim that this methodology is well-placed to deliver transformative improvements to psychiatric science. I argue that talk of a data-driven revolution in psychiatry is premature. Transformative improvements, cashed out in terms of better patient outcomes, cannot be achieved without addressing patient understanding. That is, how patients understand their own mental illnesses. I conceptualize understanding as the possession of adaptive mental constructs through which experience is mediated. I suggest that this notion of understanding serves as a bottleneck which any prospective approach to psychiatry must address to be efficacious. Subsequently I argue that, though the data-driven approach is undoubtedly powerful, it does not have a straightforward means of unblocking the bottleneck of understanding. I suggest that the data-driven approach must be supplemented with significant theoretical progress if it is to transform psychiatry.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Big data; Machine learning; Mental illness; Psychiatry; Recovery; Understanding
Institutions of the University:	Faculties > Faculty of Cultural Studies Faculties > Faculty of Cultural Studies > Department of Philosophy Faculties > Faculty of Cultural Studies > Department of Philosophy > Chair Philosophy, Computer Science and Artificial Intelligence > Chair Philosophy, Computer Science and Artificial Intelligence - Univ.-Prof. Dr. Lena Kästner
Result of work at the UBT:	Yes
DDC Subjects:	100 Philosophy and psychology > 100 Philosophy 600 Technology, medicine, applied sciences > 600 Technology
Date Deposited:	31 Jul 2023 05:46
Last Modified:	31 Jul 2023 06:13
URI:	https://eref.uni-bayreuth.de/id/eprint/86399