Decoding Mental Health Center
Research Programs

Our Research Programs

At the Decoding Mental Health Center, our goal is to develop the scientific methods and administrative framework needed to create real-time strategies that impact the individual’s and population’s mental health.

Our researchers use natural language processing and artificial intelligence (AI) techniques within a high-performance computing environment. To complete these complex analyses, we partner with the Oak Ridge National Laboratory in Tennessee—which houses the most powerful computer systems in the US.

Our current research centers on six areas of focus.

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Mental Health Trajectories Program

Our mental health research enables the earlier identification of mental health crisis, anxiety, depression and suicidal ideation in children. We use genetic traits, current physical and mental health state, social determinants of health, and environmental exposure to known indicators of risk to predict patient trajectories of anxiety, depression and suicidal ideation.

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Epilepsy Surgery Program

We use AI and machine learning to identify candidates for epilepsy surgery earlier in the disease course. Nationally, it takes an average of 10 years to be referred to an epilepsy surgical clinic. We use our machine learning algorithm to decrease the time for surgical referral to four years.

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Suicide Risk Classification Program

How people talk and think provides clues to their mental health and suicidal tendencies. We use machine learning to discover datasets that point to indications of suicidal tendencies. We created evidence-based risk assessment tools that predict suicide early and allow for positive intervention.

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Population Health Program

About 40% of mental illness begins in our youth. We use AI to harness data about geomarkers such as air pollution, community characteristics, crime, environmental exposures, psychosocial stressors and social determinants of health that can increase a population’s risk for mental health conditions.

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Virtual Humans

We are developing AI-driven virtual humans designed to support pediatric mental health care. Our first pilot, in collaboration with Dell Technologies and Uneeq, focuses on identifying depression in children with epilepsy. By combining advanced conversational AI with clinical expertise, this work aims to create safe, engaging, and scalable tools that support early detection and intervention.

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Autonomous Cultural Adaptation

A core challenge in digital health is ensuring that care feels personal and culturally sensitive. Our autonomous cultural adaptation program enables virtual humans to adjust their language, tone, and interaction style to reflect each child’s cultural background and lived experience. This adaptability strengthens trust, improves communication, and increases the effectiveness of care for diverse pediatric populations.

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