A computational approach to identifying trans-diagnostic predictors of antidepressant response
MQ Mental Health Research
Trinity College Dublin
We need to develop tools that can improve the precision with which we allocate treatments in psychiatry. Current psychiatric disease classifications (DSM-5, ICD-10) ensure reliable diagnoses across clinicians, but their diagnostic categories do not allow for individual treatment predictions – for example, most patients with major depression do not recover after their first antidepressant treatment. This project aims to remedy this by using machine learning to develop an algorithm that can quantify how likely an individual is to respond to a range of antidepressant medications. Large samples are required to train a robust predictive model. To achieve this, all data collection will be web-based and subjects will be recruited worldwide. Predictors will include cognitive tests, which are linked to specific brain circuits and neurotransmitter systems that relate to psychiatric disease and treatment action, clinical symptoms and treatment variables, and demographics. Once the algorithm has been trained and validated on an independent test set, we will test its generalizability in three local clinics. The long-term goal of this work is to develop a tool that can be used by clinicians to achieve individual treatment predictions, something that will ultimately be tested in a randomized controlled trial.
4.1 Discovery and preclinical testing of markers and technologies