Deep Learning Based Natural Language Processing Markers of Anxiety and Depression

基于深度学习的自然语言处理的焦虑和抑郁标记

• 批准号: 10723819
• 负责人: Matteo Malgaroli
• 金额: $ 19.55万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-08 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723819
• 关键词: Accounting; Address; Algorithms; Anxiety; Artificial Intelligence; Attention; Award; Behavior assessment; Caring; Classification; Clinical; Cognitive; Computational Linguistics; Data; Decision Making; Detection; Development; Diagnosis; Dictionary; Digital biomarker; Dimensions; Emotional; Engineering; Evaluation; Future; Generalized Anxiety Disorder; Goals; Health; Heterogeneity; Human; Impairment; Individual; Intervention; Interview; K-Series Research Career Programs; Language; Learning; Linguistics; Major Depressive Disorder; Measurement; Measures; Mental Depression; Mentored Patient-Oriented Research Career Development Award; Mentorship; Methodology; Methods; Modeling; Monitor; Morality; Natural Language Processing; Negative Valence; Outpatients; Patient Self-Report; Patients; Pattern; Performance; Population; Positive Valence; Protocols documentation; Psychiatry; Psychotherapy; Public Health; Quality of life; Reaction; Research; Research Domain Criteria; Rewards; Risk; Sampling; Scientific Advances and Accomplishments; Security; Source; Standardization; Symptoms; System; Testing; Training; Transcript; Validation; base; behavioral health; biomarker identification; comorbidity; deep learning; deep learning model; design; diagnostic strategy; digital health; digital tool; disability; economic cost; emotional stimulus; experience; improved; learning strategy; loss of function; multidisciplinary; neuroeconomics; novel; programs; recruit; response; screening; suicidal risk; tool; trait

PROJECT SUMMARY / ABSTRACT Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are among the primary causes of health burden worldwide. MDD is a leading cause of disability associated with increased morality risk, and both MDD and GAD result in considerable economic costs, loss of functioning, and decreased quality of life. One of the biggest challenges in responding to current calls for population-level screening is to monitor MDD and GAD at a large scale while minimizing assessment burden. Existing assessment methods, however, rely on subjective measures, are based on diagnostic approaches, and are burdensome in the extent needed to characterize MDD and GAD in their heterogeneity, which would require combined evaluation of all symptoms. New methods are needed to accurately assess behavioral health, overcome barriers to monitoring and care, and advance the scientific understanding of depression and anxiety. The proposed study aims to address these gaps by deconstructing MDD and GAD into Digital Biomarkers (DB) based on linguistic features identified by large language models. State of the art artificial intelligence and Natural Language Processing methods allow representation learning of DB from cognitive and emotional domains captured from linguistic information. While effective, passive, and at-scale monitoring are the primary benefits of DB, we will also use them to study relevant Research Domain Criteria (RDoC), including negative valence system reactions and positive valence traits. The study goals are to: 1) Design DB of MDD and GAD symptoms using deep learning methods, by training an attention-based language model on a very large corpus of de-identified psychotherapy treatment transcripts; 2) Examine preliminary performance and feasibility of the DB model in a highly characterized sample of MDD and GAD patients, and compare results with clinician ratings; 3) Explore improvements to the DB model based on research paradigms consistent with RDoC constructs, to further refine DB model pipeline and future deployment in clinical settings. The program of research and training described in this mentored patient-oriented research career development award is aimed at developing systematic digital health approaches to allow dimensional conceptualization of MDD and GAD consistent with RDoC, enhancing the ease and consistency of detection to ultimately support targeted interventions. The proposed project is strongly supported by a multidisciplinary team including the mentorship of Drs. Naomi Simon and Kyunghyun Cho, and the domain expertise of Drs. Paul Glimcher, Tim Althoff, Zhe Chen, and Tanzeem Choudhury. The experience gained from the award will enable the pursuit of future R-level studies focusing on advanced computational psychiatry approaches to further refine DB models to improve passive and objective assessment of behavioral health, and ultimately improve our empirical understanding of depression and anxiety.

项目概要/摘要 重度抑郁症（MDD）和广泛性焦虑症（GAD）是主要的抑郁症 造成全世界健康负担的原因。 MDD 是与道德提高相关的残疾的主要原因 风险，MDD 和 GAD 都会导致相当大的经济成本、功能丧失和质量下降 的生活。响应当前人群筛查呼吁的最大挑战之一是监测 大规模的 MDD 和 GAD，同时最大限度地减少评估负担。然而现有的评估方法 依赖主观测量，基于诊断方法，并且在所需程度上是繁重的 描述 MDD 和 GAD 的异质性，这需要对所有 症状。需要新方法来准确评估行为健康，克服监测障碍 和护理，促进对抑郁和焦虑的科学认识。 拟议的研究旨在通过将 MDD 和 GAD 解构为数字化来解决这些差距 基于大型语言模型识别的语言特征的生物标记 (DB)。最先进的人工 智能和自然语言处理方法允许从认知和自然语言中进行数据库的表示学习 从语言信息中捕获的情感领域。虽然有效、被动和大规模的监测是 DB 的主要好处，我们还将使用它们来研究相关的研究领域标准 (RDoC)， 包括负价系统反应和正价特征。研究目标是： 1）设计数据库 使用深度学习方法，通过在基于注意力的语言模型上训练 MDD 和 GAD 症状 非常大的未识别身份的心理治疗笔录语料库； 2) 检查初步性能 DB 模型在 MDD 和 GAD 患者的高度特征化样本中的可行性，并进行比较 具有临床医生评级的结果； 3）基于研究范式探索DB模型的改进 与 RDoC 结构一致，进一步完善数据库模型管道和未来在临床环境中的部署。 这个以患者为导向的研究生涯中描述的研究和培训计划 发展奖旨在开发系统的数字健康方法，以实现维度 MDD 和 GAD 的概念化与 RDoC 一致，增强了检测的简易性和一致性 最终支持有针对性的干预措施。拟议的项目得到了多学科的大力支持 团队，包括博士的指导。 Naomi Simon 和 Kyunghyun Cho 以及博士的领域专业知识。 保罗·格里姆彻、蒂姆·阿尔索夫、陈哲和坦泽姆·乔杜里。从该奖项中获得的经验将 促进未来 R 级研究的重点是先进的计算精神病学方法 进一步完善DB模型，以改善行为健康的被动和客观评估，最终 提高我们对抑郁和焦虑的实证理解。