This technology is a machine learning software for digital phenotyping of mental health illnesses like anxiety and depression that synchronizes neural recordings, wearable sensors, and phone data.
Current methods to assess and diagnose mental health and psychiatric state rely on subjective patient assessments and physician interpretation, which can be time-consuming and lead to inaccurate diagnoses. Digital and offline platforms show promise in monitoring physiological and behavioral states, but high dimensional analysis and phenotyping remains challenging in the context of mental health. There are no assessment platforms that can monitor multidimensional patient data while simultaneously offering accurate assessment and diagnosis for anxiety, depression, and other related psychiatric disorders.
This platform collects physiological and behavioral data from patients and is equipped with a machine learning algorithm for high dimensional phenotyping and assessment of psychiatric state. Neural recordings, wearable sensors, and smartphone data are integrated and synchronized in this software platform. Ultimately, this integrated software uses multimodal data to predict anxiety level, cognitive performance, and human behavior and can conduct rapid risk assessments.
IR CU24388
Licensing Contact: Dovina Qu
