Awarded a prestigious NIMH RO1 grant with our collaborators at Boston University and Kaiser Permanente. This grant will leverage machine learning to optimise user engagement and clinical response to digital mental health interventions, which will in turn inform optimal treatment allocation.
ROI NMIH GRANT:
Leveraging Machine Learning for Precision Medicine
Using machine learning, we have recently developed a prediction tool together with Microsoft Research that generates an early highly accurate prediction, regarding the probability that a patient is likely to significantly improve or not at the end of treatment. We are currently carrying out a randomised controlled trial in partnership with Berkshire Healthcare NHS Foundation Trust to evaluate the performance and acceptability of this tool for supporting health professionals with their clinical decision making.
Mental health and Machine Learning
Collaborating with researchers in Mexico and Harvard University on a large-scale randomised controlled trial in Latin America to evaluate our culturally-adapted Spanish digital CBT program for depression and anxiety in university students. A key objective of this trial is to determine which treatment pathways (guided versus self-guided) and which baseline characteristics are linked to better outcomes.
Mexico and Colombia (NIMH)
Collaborating with researchers at Harvard and West Virginia University to support a three-arm randomised control trial to evaluate the efficacy of adjunctive digital CBT for treating depression in rural communities in Appalachia in the Eastern United States. A key aim of this trial is to develop a stable individualized treatment rule that will allow patients and treatment providers to improve treatment outcomes by deciding collaboratively when standard primary care treatment should be augmented with digital CBT.
Appalachia Mind Health Initiative (AMHI)