Collaborative Research: SCH: Using Multi-Stage Learning to Prioritize Mental Health Risk Using Evidence from Speech and Text

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因素，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心理健康检测和监测问题的根本转变，通过一项技术调查，将语音分析、语言分析和机器学习研究结合在一起，以丰富的临床经验和专业知识为基础，并以伦理收集的数据为动力。将使用一个分级的多分支机构框架来提供一种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这一现实问题。调查将包括模拟患者在临床访问之间的监控，这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控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，在竞争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的多武装匪徒方案，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，但也有成本。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，病人接触通常从接待员、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，如单调子模块)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，虽然直接受到心理健康环境的激励和测试，但将在医疗保健的其他环境中以及其他发挥“优先排序漏斗”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, mentalhealth issues are a top contributor to global disease and a leading cause of disability worldwide. It is anenormous personal and societal toll. Mental illness is a common precursor to suicide, and suicidality isthe second leading cause of death in youth and young adults between 10 and 34 years of age. Ineconomic terms, mental illness exceeds cardiovascular diseases in the projected 2011-2030 cost ofnoncommunicable diseases (USD16.3T worldwide). Complicating this picture further is the fact thatmental healthcare is desperately resource-limited, and clinicians treating people for mental healthproblems operate in a vacuum between visits. This project proposes a fundamental shift in how machinelearning is used to approach the problem of mental health detection and monitoring, with a technologicalinvestigation 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 multiarmedbandit framework will be used to provide a highly flexible way to evaluate multiple kinds ofevidence in settings where there can be diverse methods for assessment that vary in cost and the value ofthe information they provide. As such, it is an excellent fit for the real-world problem of mental healthassessment in resource-limited settings. Investigations will include simulations of patient monitoringbetween 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 inmachine learning is a good fit for the real-world scenario that mental health providers face whenmonitoring a population of patients in treatment: what is the best way to allocate limited resources amongcompeting choices, given only limited information? This project develops a tiered multi-armed banditformulation, where a succession of stages is applied to a population of patients in order to best allocatedifferent types of resources, each with different per-patient impact but also cost. Conceptually, tieredapproaches are familiar in current medical practice. For example, patient contact typically progressesfrom a receptionist, to a nurse or intake coordinator, perhaps to a certified nurse practitioner, to a primarycare doctor, ultimately to a specialist---each step involving corresponding increases in both the cost of theprofessional involved and their degree of expertise. The tiered multi-armed bandit model developed bythis award includes concerns of stochastic and adverse selection, where patients at one tier do not proceeddeterministically to the next, even when explicitly selected. It also incorporates complex (e.g., non-linearsuch as monotone submodular) objective functions that better capture within-cohort interactions. Onecore strength of the tiered model is that it provides a flexible way to incorporate multiple kinds ofevaluative evidence in settings where there can be diverse methods for assessment that vary in cost andthe value of the information they provide. Toward that end, this project also includes both text analysisand speech analysis components that make use of ethically collected language and speech data andclinically validated assessments of mental condition. Techniques developed under this award, whiledirectly motivated by and tested in the mental health setting, will be useful in other settings in bothhealthcare as well as other settings where a "prioritization funnel" is in play, including talent sourcing andcustomer 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.