Deep Learning for Adaptive Continuous Care to Common Mental Health Disorders
This project combines expertise from Clinical Psychology and Computer Science to develop and implement Machine Learning methods to improve adherence (rate of patients who complete treatment), clinical efficiency (time spent by clinician per patient), and treatment efficacy (how effective the treatment are at alleviating symptoms of mental distress/illness) in the delivery of (Braive's) digital, iCBT supported psychotherapy solutions. Braive will bring to market a new patient-centric and R&D-driven solution that will address the shortcomings of current iCBT solutions. To meet this goal, we will create a new generation of iCBT that gradually automates the timely response to a patient's development in treatment. Our new system - AVA, short for Automated Vigilance Assistant - will be able to: - Take patient's guided inputs from clinically validated Mental Health Check (MHC) tool and support clinical decision-making by remote therapists, using quantitative scores and qualitative analysis; - Understand patients? notes and queries through deep-learning and natural language understanding systems that; - Monitor and detect deviations from treatment trajectories by interpreting written input and analyzing sporadic queries with patients to assess compliance and do sentiment analysis; - Trigger human- or AI-led interventions targeting each patient and the observed deviation from treatment trajectory.