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

合作研究：SCH：利用语音和文本证据，利用多阶段学习来优先考虑心理健康风险

• 批准号: 2124224
• 负责人: Deanna Kelly
• 金额: $ 30.76万
• 依托单位: University of Maryland at Baltimore
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124224&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; Using; Multi

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.

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首要因素，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岁青年和年轻人死亡的第二大原因。从经济角度来看，在2011-2030年非传染性疾病的预计成本中，精神疾病超过了心血管疾病（全球16.3万亿美元）。使这一情况进一步复杂化的是，精神卫生保健资源极其有限，临床医生治疗精神健康问题的人在访问之间的真空中运作。该项目提出了如何使用机器学习来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在一起，以深厚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。一个分层的多臂强盗框架将被用来提供一个高度灵活的方式来评估多种证据的设置，可以有不同的评估方法，在成本和价值的信息，他们提供的不同。因此，它非常适合在资源有限的环境中进行心理健康评估的现实问题。调查将包括模拟临床访视之间的患者监测，这些监测将根据现实的、真实世界的假设和团队成员治疗精神分裂症、抑郁症、该项目技术方法的核心是认识到机器学习中的“多臂强盗”问题非常适合真实的-心理健康提供者在监测接受治疗的患者人群时面临的世界情景：在只有有限的信息的情况下，如何分配有限的资源以应对相互竞争的选择？该项目开发了一个分层的多武装土匪公式，其中一系列阶段适用于患者人群，以便最好地分配不同类型的资源，每个阶段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很常见。例如，与病人的接触通常从接待员，到护士或入院协调员，也许到执业护士，到初级保健医生，最后到专家-每一步都涉及相应的专业人员费用和专业程度的增加。该奖项开发的分层多臂强盗模型包括随机和逆向选择的关注，其中一个层次的患者不会确定性地进行到下一个层次，即使明确选择。它还包括复杂的（例如，非线性（例如单调次模）目标函数，其更好地捕获群组内的相互作用。分层模型的一个核心优势在于，它提供了一种灵活的方法，可以在各种评估方法的环境中整合多种评估证据，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，这些组件利用道德收集的语言和语音数据以及临床验证的精神状况评估。在这个奖项下开发的技术，虽然直接受到心理健康环境的激励和测试，但在医疗保健以及其他“优先漏斗”发挥作用的环境中也将是有用的，包括人才采购和客户获取。这个奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。