Machine learning platform for objective mental health assessment and diagnosis
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.
Unmet Need: Machine learning software for unbiased, automated mental health assessment
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.
The Technology: Objective assessment and diagnostic software for mental and psychiatric illness
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.
Applications:
- Monitoring software for mental health and behavior
- Research tool for studying anxiety, depression, and other mental health disorders
- Behavioral monitoring and consumer use tool for targeted advertising
- Biological sensor for healthcare monitoring
- Machine learning software for automated healthcare applications
- Diagnostic tool for mental health applications or other diseases with behavioral and cognitive signatures
Advantages:
- Automated and unbiased software
- Integrates physiological, behavioral, and environmental context data from multiple sources
- Reduces reliance on subjective assessments of mental health
- Increases behavioral and cognitive risk predictions
- Increases treatment outcomes for anxiety and psychiatric illness
- Accessible, digital platform
Lead Inventor:
Related Publications:
Tech Ventures Reference:
IR CU24388
Licensing Contact: Dovina Qu