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

根据世界卫生组织和2010年全球疾病负担研究，心理健康问题是全球疾病的主要原因，也是全球残疾的主要原因。这是一个巨大的个人和社会代价。精神疾病是自杀的常见前兆，自杀是10至34岁青年和青壮年死亡的第二大原因。从经济角度来看，在2011-2030年非传染性疾病预计成本中，精神疾病超过心血管疾病（全球16.3万亿美元）。使这一情况进一步复杂化的事实是，精神卫生保健资源极其有限，治疗精神卫生问题的临床医生在两次就诊之间处于真空状态。该项目提出了机器学习如何用于解决心理健康检测和监测问题的根本转变，通过技术调查将语音分析，语言分析和机器学习研究结合起来，以深厚的临床经验和专业知识为依据，并以道德收集的数据为动力。将采用分层的多武装盗匪框架，以提供一种高度灵活的方式，在可能存在各种评估方法的环境中评估多种证据，这些方法的成本和所提供信息的价值各不相同。因此，它非常适合在资源有限的环境中进行精神健康评估的现实问题。调查将包括模拟临床访问之间的患者监测，这将由现实世界的假设和团队成员治疗精神分裂症、抑郁症和自杀风险患者的临床经验提供信息。该项目技术方法的核心是认识到机器学习中的“多臂强盗”问题非常适合心理健康提供者在监测治疗中的患者群体时面临的现实场景：在有限的信息下，在竞争选择中分配有限资源的最佳方法是什么？该项目开发了一种分层的多臂强盗配方，其中将连续的阶段应用于患者群体，以便最佳地分配不同类型的资源，每种资源对每位患者的影响不同，但成本也不同。从概念上讲，分层方法在当前的医疗实践中是熟悉的。例如，患者接触通常从前台到护士或接收协调员，可能到认证执业护士，到初级保健医生，最终到专家——每一步都涉及相应的专业人员成本和他们的专业程度的增加。该奖项开发的分层多臂强盗模型包括对随机和逆向选择的关注，其中一层的患者即使被明确选择，也不会确定地进入下一层。它还结合了复杂的（例如，非线性的，如单调子模）目标函数，可以更好地捕获队列内的相互作用。分层模式的一个核心优势是，它提供了一种灵活的方式，在可能存在各种评估方法的环境中纳入多种评估证据，这些方法的成本和所提供信息的价值各不相同。为此，该项目还包括文本分析和语音分析组件，这些组件利用伦理收集的语言和语音数据以及临床验证的精神状况评估。在该奖项下开发的技术，虽然直接受到心理健康环境的激励和测试，但将在医疗保健和其他“优先次序漏斗”发挥作用的环境中发挥作用，包括人才招聘和客户获取。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.