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Simple, scalable, and interpretable risk stratification in psychiatry using electronic health records
With the emergence of evidence-based treatments for treatment-resistant depression, strategies to identify individuals at greater risk for treatment resistance early in the course of illness could have clinical utility. We sought to develop and validate a model to predict treatment resistance in major depressive disorder using coded clinical data from the electronic health record. We identified individuals from a large health system with a diagnosis of major depressive disorder receiving an index antidepressant prescription, and used a tree-based machine learning classifier to build a risk stratification model to identify those likely to experience treatment resistance. The resulting model was validated in a second health system.
Electronic health records facilitated stratification of risk for treatment-resistant depression and demonstrated generalizability to a second health system. Efforts to improve upon such models using additional measures, and to understand their performance in real-world clinical settings, are warranted.
Date:
26 April 2022, 15:00
Venue:
via Zoom (please email to get a link or consider subscribing to mailing list here: web.maillist.ox.ac.uk/ox/info/ai4mch)
Speaker:
Professor Roy H. Perlis (Harvard Medical School)
Organising department:
Department of Psychiatry
Organiser:
Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address:
andrey.kormilitzin@psych.ox.ac.uk
Host:
Dr Andrey Kormilitzin (University of Oxford)
Part of:
Artificial Intelligence for Mental Health Seminar Series
Booking required?:
Recommended
Booking url:
https://web.maillist.ox.ac.uk/ox/info/ai4mch
Booking email:
andrey.kormilitzin@psych.ox.ac.uk
Audience:
Public
Editor:
Andrey Kormilitzin
