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

SCH：INT：协作研究：利用多阶段学习优先考虑心理健康

基本信息

批准号：
2124270
负责人：
Carol Espy-Wilson
金额：
$ 84.24万
依托单位：
University of Maryland, College Park
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=2124270&HistoricalAwards=false
关键词：
SCH INT Collaborative Research Using

项目摘要

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.

根据世界卫生组织和 2010 年全球疾病负担研究，精神健康问题是导致全球疾病的主要原因，也是造成全球残疾的主要原因。精神疾病是自杀的常见前兆。，自杀是 10 至 34 岁青少年的第二大死亡原因。从经济角度来看，预计 2011 年至 2030 年非传染性疾病造成的损失中，精神疾病将超过心血管疾病。（全球 16.3T 美元）使这一情况进一步复杂化的是，心理医疗保健的资源极其有限，并且第一年的心理健康问题治疗是在两次就诊之间的真空中进行的。该项目提出了机器学习方式的根本转变。用于解决心理健康检测和监测问题，通过将语音分析、语言分析和机器学习研究结合在一起的技术调查，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推动力。将用于提供在存在不同成本和所提供信息价值的不同评估方法的环境中评估多种证据的高度灵活的方式，因此非常适合现实世界的心理健康评估问题。在资源有限的环境中，调查将包括在临床就诊之间模拟患者监测，这些模拟将根据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。项目的技术方法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所面临的现实世界场景：在仅考虑有限资源的情况下，在竞争性选择中分配有限资源的最佳方式是什么？该项目开发了一种分层多臂老虎机方案，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，每个阶段对每个患者都有不同的影响，但从概念上讲，也是分层方法。目前熟悉的例如，与患者的接触通常是从接待员到护士或住院协调员，也许是经过认证的执业护士，到初级保健医生，最终到专家——每一步都涉及相应的成本增加。该奖项开发的分层多臂老虎机模型包括对随机和逆向选择的关注，其中一层的患者即使明确选择也不会确定地进入下一层。复杂（例如，分层模型的一个核心优势是，它提供了一种灵活的方式，可以在可以有多种评估方法的环境中纳入多种评估证据。为此，该项目还包括文本分析和语音分析组件，这些组件利用了道德收集的语言和语音数据以及根据该奖项开发的经过临床验证的技术。 ,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发挥“优先顺序漏斗”作用的环境中也将很有用，包括人才采购和客户获取。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

DOI：
10.24963/ijcai.2022/51
发表时间：
2022-02
期刊：
ArXiv
影响因子：
0
作者：
Marina Knittel;Samuel Dooley;John P. Dickerson
通讯作者：
Marina Knittel;Samuel Dooley;John P. Dickerson

Multimodal Depression Severity Score Prediction Using Articulatory Coordination Features and Hierarchical Attention Based Text Embeddings

DOI：
10.21437/interspeech.2022-11099
发表时间：
2022-09
期刊：
影响因子：
0
作者：
Nadee Seneviratne;C. Espy-Wilson
通讯作者：
Nadee Seneviratne;C. Espy-Wilson

Forecasting Patient Outcomes in Kidney Exchange

预测肾脏交换的患者结果

DOI：
10.24963/ijcai.2022/701
发表时间：
2022
期刊：
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
影响因子：
0
作者：
Durvasula, Naveen;Srinivasan, Aravind;Dickerson, John
通讯作者：
Dickerson, John

Robustness Disparities in Face Detection

DOI：
10.48550/arxiv.2211.15937
发表时间：
2022-11
期刊：
ArXiv
影响因子：
0
作者：
Samuel Dooley;George Z. Wei;T. Goldstein;John P. Dickerson
通讯作者：
Samuel Dooley;George Z. Wei;T. Goldstein;John P. Dickerson

Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting

规划公平分配：不安定强盗环境中的概率公平

DOI：
10.1145/3580305.3599467
发表时间：
2023
期刊：
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Herlihy, Christine;Prins, Aviva;Srinivasan, Aravind;Dickerson, John P.
通讯作者：
Dickerson, John P.