Our client is bringing new therapeutics to market for neurobehavioral disorders and needed a better way to diagnose patients with depression and anxiety based on video and audio content. A platform was needed to enable a digital diagnosis to support patient stratification for clinical trials and assess the efficacy of treatment.
We partnered with their data science and software engineering teams to design and build a platform to use machine learning to improve the diagnosis of patients with depression and anxiety. Time series audio and video data, captured via a mobile app, were feature engineered to train a model for predicting clinical outcomes. Improved diagnosis will enhance the stratification of patients for trial enrollment and accelerated assessment of therapeutic response.