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，精神健康问题是全球疾病的主要贡献者，也是全球残疾的主要原因。这对个人和社会都是巨大的损失。精神疾病是自杀的常见先兆，自杀是年轻人和10至34岁年轻人死亡的第二大原因。在经济方面，在2011-2030年非传染性疾病的预测成本中，精神疾病超过心血管疾病(全球16.3T美元)。使情况进一步复杂化的是，精神卫生保健极其有限的资源，治疗精神健康问题的临床医生在两次就诊之间的真空中运作。该项目提出了利用机器学习来处理精神健康检测和监测问题的根本转变，通过一项结合了语音分析、语言分析和机器学习研究的技术调查，以深入的临床经验和专业知识为基础，并以道德收集的数据为动力。一个分级的多臂盗贼框架将被用来提供高度灵活的方式来评估多种证据，在这种情况下，可以有不同的评估方法，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情况下进行心理健康评估的现实问题。调查将包括模拟临床就诊之间的患者监测，这些模拟将由现实的现实世界假设和团队成员治疗患有精神分裂症、抑郁症和自杀风险的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”问题非常适合精神卫生提供者在监测治疗中的患者群体时所面临的现实场景：在信息有限的情况下，在相互竞争的选择中分配有限的资源的最佳方式是什么？该项目开发了一种分级多臂强盗方案，其中将一系列阶段应用于患者群体，以便最好地分配不同类型的资源，每种资源对每个患者的影响不同，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很常见的。例如，患者接触通常是从接待员、护士或收容协调员，也许是注册护士从业者，到初级保健医生，最终到专科医生-每一步都涉及所涉及的专业人员的成本和他们的专业程度的相应增加。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担忧，在这种情况下，即使明确选择，一级患者也不会确定性地进入下一级。它还结合了复杂的(例如，非线性的，如单调子模数)目标函数，更好地捕捉队列内的相互作用。分级模式的一个核心优点是，它提供了一种灵活的方式，可以在可以有不同的评估方法的情况下纳入多种评估证据，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，这些组件利用从伦理角度收集的语言和语音数据以及经临床验证的精神状况评估。根据该奖项开发的技术，虽然直接受到精神健康环境的激励和测试，但将在医疗保健以及其他发挥作用的环境中有用，包括人才寻找和客户获取。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.