SBIR Opportunity: Language Models for Veteran Suicide Prevention (LM4VSP)

On September 22, 2021, the US House Veterans Affairs Committee held a hearing entitled, “Veteran Suicide Prevention: Innovative Research and Expanded Public Health Efforts” [1]. The hearing followed the release of annual data from the US Department of Veterans Affairs showing that the disproportionate rate of veteran suicide is a public health crisis [2]. Although there is no single reason why veterans commit suicide, evidence suggests that stable housing, financial security, access to healthcare, addressing social isolation and loneliness, and treating the effects of trauma are important components of a comprehensive suicide prevention strategy; all of which require coordination and cooperation across families, communities, and at all levels of government. Recent advances in AI, and specifically LMs, have the potential to help lessen the effects of social isolation/loneliness and trauma. According to a 2019 report on “Sleep and timing of death by suicide among US Veterans 2006-2015” [McCarthy, et al. 2019], the raw proportion of veteran suicides peaks between the hours of 1000 and 1200; however, the peak prevalence of suicide, after accounting for the population being awake, is between the hours of 0000 and 0300 (p < 0.00001, F = 0.88). The highest Standardized Incidence Ratio (SIR) is at midnight; US Veterans are eight times more likely to die by suicide than expected given the population awake (SIR = 8.17; 95% Confidence Interval = 7.45-8.94). In other words, when clinical help is likely unavailable or difficult to access, technology has the potential to provide critical assistance. Recent advances in the field of natural language processing have allowed LMs (for example, Chat GPT (Generative Pre-trained Transformer)) to be fine-tuned using reinforcement learning based on human feedback [3]. Prior efforts have shown that it is possible to create a highly conversational model based on 40,000 pieces of feedback [4]. Other recent research work in the field suggests promising results in prompt engineering [5], using memory-based machine learning with dramatic improvements in the LM’s ability to stay on task, and return more accurate and precise results. [6] LM4VSP seeks to develop a clinical co-pilot based on LMs specific to the mental health subdomain, and in close collaboration with mental health subject matter experts (SMEs). The goal is for the LM4VSP clinical co-pilot to enable caregivers to offer around-the-clock assistance and accelerate their understanding and assessments and improve the effectiveness of intervention.