Identification and Prediction of Peripartum Depression from Natural Language Collected in a Mobile Health App

根据移动健康应用程序收集的自然语言识别和预测围产期抑郁症

• 批准号: 9892136
• 负责人: Tamar Krishnamurti
• 金额: $ 23.38万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-19 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9892136
• 关键词: Address; Affect; African American; Algorithms; Appointment; Behavior; Behavioral Sciences; Birth; Caring; Caucasians; Characteristics; Child; Childbirth; Clinical; Collaborations; Collection; Data; Data Collection; Depressed mood; Detection; Developmental Delay Disorders; Diagnostic; Disclosure; Early Diagnosis; Early treatment; Emotions; Environment; Event; Failure to Thrive; Feeling; Foundations; Frequencies; Healthcare Systems; Incidence; Infant; Intervention; Journals; Language; Longitudinal observational study; Longitudinal prospective study; Measurable; Measurement; Measures; Mental Depression; Mental Health; Methodology; Methods; Mobile Health Application; Modeling; Monitor; Moods; Mothers; National Institute of Mental Health; Natural Language Processing; Participant; Patients; Pattern; Perinatal; Phenotype; Physicians; Population; Postpartum Depression; Postpartum Period; Pregnancy; Pregnant Women; Premature Birth; Prenatal care; Psychometrics; Race; Reporting; Research; Risk; Risk Assessment; Scientist; Signal Transduction; Societies; Source; Stress; Technology; Text; Time; Variant; Visit; Voice; Well in self; Woman; Work; antepartum depression; base; cohort; cost; depression model; depressive symptoms; experience; fetal; health assessment; improved; innovation; longitudinal design; machine learning algorithm; member; motherhood; natural language; patient subsets; peripartum depression; physical conditioning; pregnant; racial disparity; response; routine screening; smartphone Application; social culture; sociodemographics; statistical and machine learning; time use; vector

PROJECT SUMMARY Background: Depression during pregnancy and the postpartum period affects up to 15% of US mothers, imposing costs on mother, child, and society. Early detection can significantly reduce the incidence of depression, yet depressive symptoms are often missed during prenatal visits, which tend to focus on maternal and fetal physical health, leaving less time for maternal mental health. Even if mental health is addressed during prenatal care, women may not feel comfortable answering questions that are perceived to be embarrassing or invasive. Failing to detect depression is even more likely during the postpartum period due to infrequent physician visits once the baby has been born. Measurement in the form of daily journals, which can be analyzed using natural language processing, can promote early and more frequent detection of depression during pregnancy and the postpartum period. Study Aims: 1) Model which dynamic features of language used over time best predict changes in depression status in the pregnancy and postpartum periods, creating phenotypes of depression risk; 2) examine how the language patterns that predict depression differ for African-American and Caucasian women; and 3) identify the relationship between the characteristics of what depressed peripartum women say and their treatment-seeking behavior. Innovation: The proposed research is innovative in its use of high frequency natural language measurements, captured in daily journals using a smartphone app, combined with advances in natural language processing models, to assess the onset and trajectory of depression during pregnancy and the postpartum period. This is the first prospective longitudinal study using natural language collection for risk prediction in a clinical population and the first to: 1) characterize the critical topics women discuss during the peripartum period over time using open-ended journals; 2) evaluate multiple facets of language to gain a more comprehensive understanding of the relationship between language and depression; 3) use a longitudinal design approach allowing for optimal modeling of language changes associated with depression onset. Methodology and Expected Results: Monthly depression risk identified from the Edinburgh Postnatal Depression Scale. will be collected through the MyHealthyPregnancy smartphone app, a mobile health application developed through close collaboration between decision scientists, clinicians, statisticians, and local peripartum women. A daily journal embedded in the MyHealthyPregnancy app will collect natural language text from the participants for 10 months (from their first prenatal visit through two months postpartum). Using three distinct natural language processing algorithmic approaches, this study will characterize how the natural language used by peripartum women in their daily journal entries is connected to the onset and experience of peripartum depression, as measured through monthly-administered depression scales. Group- based trajectory modeling will then classify women according to the patterns in their depression scores over time. Potential Impact: This work lays the foundation for developing and evaluating real-time interventions that could be deployed at scale to women who are using language that signals high depression risk.

项目摘要 背景：怀孕期间和产后期间的抑郁症影响了多达15%的美国母亲， 母亲，孩子，社会。早期发现可以显著降低抑郁症的发病率，但抑郁症状 在产前检查中经常被错过，产前检查往往侧重于母亲和胎儿的身体健康， 产妇心理健康即使在产前护理中提到了心理健康问题， 被认为是尴尬或侵犯性的问题。在这段时间里， 产后期，因为婴儿出生后医生很少去看。以日报的形式计量， 可以使用自然语言处理进行分析，可以促进早期和更频繁地检测抑郁症， 在怀孕和产后期间。 研究目的：1）随着时间的推移，语言使用的动态特征最能预测抑郁状态变化的模型， 怀孕和产后期间，创造抑郁症风险的表型; 2）检查语言模式， 预测抑郁症在非洲裔美国人和白人妇女中的差异; 3）确定 抑郁的围产期妇女说什么和他们的治疗寻求行为的特点。 创新：拟议的研究在使用高频自然语言测量方面具有创新性， 使用智能手机应用程序，结合自然语言处理模型的进步，以评估发病 以及孕期和产后抑郁症的发展轨迹。这是第一个前瞻性纵向研究 在临床人群中使用自然语言收集进行风险预测，并首先：1）表征关键主题 妇女讨论在围产期随着时间的推移使用开放式期刊; 2）评估语言的多个方面 为了更全面地了解语言与抑郁症之间的关系; 3）使用纵向 设计方法允许与抑郁症发作相关的语言变化的最佳建模。 方法和预期结果：根据爱丁堡产后抑郁量表确定每月抑郁风险。 将通过MyHealthyPregnancy智能手机应用程序收集，这是一款通过密切合作开发的移动的健康应用程序。 决策科学家、临床医生、统计学家和当地围产期妇女之间的合作。一份嵌入式日报 在MyHealthyPregnancy应用程序将收集自然语言文本从参与者为10个月（从他们的第一个 产前检查至产后两个月）。使用三种不同的自然语言处理算法方法， 这项研究将描述围产期妇女在日常日记条目中使用的自然语言是如何连接的， 与围产期抑郁症的发病和经历有关，通过每月管理的抑郁量表进行测量。组- 然后，基于轨迹模型的研究将根据女性抑郁评分随时间的变化模式对她们进行分类。 潜在影响：这项工作为开发和评估实时干预措施奠定了基础， 大规模地应用于那些使用高抑郁风险语言的女性。