SCH: INT: Collaborative Research: Using Multi-Stage Learning to Prioritize Mental Health

According to the World Health Organization and the Global Burden of Disease 2010 studies, mental health issues are a top contributor to global disease and a leading cause of disability worldwide. It is an enormous personal and societal toll. Mental illness is a common precursor to suicide, and suicidality is the second leading cause of death in youth and young adults between 10 and 34-years of age.

In economic terms, mental illness exceeds cardiovascular diseases in the projected 2011-2030 cost of noncommunicable diseases (USD16.3T worldwide). Complicating this picture further is the fact that mental healthcare is desperately resource-limited, and clinicians treating people for mental health problems operate in a vacuum between visits. This project proposes a fundamental shift in how machine learning is used to approach the problem of mental health detection and monitoring, with a technological investigation that brings together speech analysis, language analysis, and machine learning research, informed by deep clinical experience and expertise and fueled by ethically collected data.

A tiered multiarmed bandit framework will be used to provide a highly flexible way to evaluate multiple kinds of evidence in settings where there can be diverse methods for assessment that vary in cost and the value of the information they provide. As such, it is an excellent fit for the real-world problem of mental health assessment in resource-limited settings.

Investigations will include simulations of patient monitoring between clinical visits that will be informed by realistic, real-world assumptions and team members' clinical experience treating patients with schizophrenia, depression, and risk of suicide.

At the core of this project's technical approach is the recognition that the “multi-armed bandit” problem in machine learning is a good fit for the real-world scenario that mental health providers face when monitoring a population of patients in treatment: what is the best way to allocate limited resources among competing choices, given only limited information? This project develops a tiered multi-armed bandit formulation, where a succession of stages is applied to a population of patients in order to best allocate different types of resources, each with different per-patient impact but also cost.

Conceptually, tiered approaches are familiar in current medical practice. For example, patient contact typically progresses from a receptionist, to a nurse or intake coordinator, perhaps to a certified nurse practitioner, to a primary care doctor, ultimately to a specialist---each step involving corresponding increases in both the cost of the professional involved and their degree of expertise.

The tiered multi-armed bandit model developed by this award includes concerns of stochastic and adverse selection, where patients at one tier do not proceed deterministically to the next, even when explicitly selected. It also incorporates complex (e.g., non-linear such as monotone submodular) objective functions that better capture within-cohort interactions.

One core strength of the tiered model is that it provides a flexible way to incorporate multiple kinds of evaluative evidence in settings where there can be diverse methods for assessment that vary in cost and the value of the information they provide. Toward that end, this project also includes both text analysis and speech analysis components that make use of ethically collected language and speech data and clinically validated assessments of mental condition.

Techniques developed under this award, while directly motivated by and tested in the mental health setting, will be useful in other settings in both healthcare as well as other settings where a "prioritization funnel" is in play, including talent sourcing and customer acquisition.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.