MIND: A tool for mental health screening and support of therapy to improve clinical and research outcomes

Routine experiences of daily living invoke particular patterns that can be detected in online activities. Every time an individual carries out any activity on the internet some kind of metadata, reflecting the user's preference, is created and stored. The generated metadata, a latent bi-product of high volume user interactions, is rich, has the potential to be mined for understanding one's current mental state. For example, Google logs every search query made on Google Search, Maps, and YouTube. Closely monitoring these experiences and events, along with the history of online activities, can inform systems to provide early diagnosis and detection of depression, anxiety, and related problems. A growing body of research focuses on using social media for identifying signals associated to various mental health phenomena. However, interventions based on such sources tend to have high false positive rates and may lead to inaccurate diagnosis. In this work, we propose a framework, MIND, that can leverage large amount of passively sensed online engagements history to estimate mental health assessments on depression, anxiety, self-esteem, etc. MIND is designed to use these otherwise ignored data, with informed consent from the subject. We envision that MIND has the potential to be easily be integrated into applications in clinical and research settings to help caregivers make informed assessments about individuals during and in between appointments and other health sector contacts.