干旱区生态环境知识资源中心(Arid): Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Arid

DOI	10.3389/fpsyg.2022.811517
	Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population
	Lin, David; Nazreen, Tahmida; Rutowski, Tomasz; Lu, Yang; Harati, Amir; Shriberg, Elizabeth; Chlebek, Piotr; Aratow, Michael
通讯作者	Aratow, M
来源期刊	FRONTIERS IN PSYCHOLOGY
ISSN	1664-1078
出版年	2022
卷号	13
英文摘要	BackgroundDepression and anxiety create a large health burden and increase the risk of premature mortality. Mental health screening is vital, but more sophisticated screening and monitoring methods are needed. The Ellipsis Health App addresses this need by using semantic information from recorded speech to screen for depression and anxiety. ObjectivesThe primary aim of this study is to determine the feasibility of collecting weekly voice samples for mental health screening. Additionally, we aim to demonstrate portability and improved performance of Ellipsis' machine learning models for patients of various ages. MethodsStudy participants were current patients at Desert Oasis Healthcare, mean age 63 years (SD = 10.3). Two non-randomized cohorts participated: one with a documented history of depression within 24 months prior to the study (Group Positive), and the other without depression (Group Negative). Participants recorded 5-min voice samples weekly for 6 weeks via the Ellipsis Health App. They also completed PHQ-8 and GAD-7 questionnaires to assess for depression and anxiety, respectively. ResultsProtocol completion rate was 61% for both groups. Use beyond protocol was 27% for Group Positive and 9% for Group Negative. The Ellipsis Health App showed an AUC of 0.82 for the combined groups when compared to the PHQ-8 and GAD-7 with a threshold score of 10. Performance was high for senior participants as well as younger age ranges. Additionally, many participants spoke longer than the required 5 min. ConclusionThe Ellipsis Health App demonstrated feasibility in using voice recordings to screen for depression and anxiety among various age groups and the machine learning models using Transformer methodology maintain performance and improve over LSTM methodology when applied to the study population.
英文关键词	mental health screening machine learning smartphone speech NLP artificial intelligence behavioral health monitoring biomarkers
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SSCI
WOS记录号	WOS:000807951200001
WOS关键词	TREATMENT OUTCOMES ; HEALTH ; SYMPTOMS ; DISTRESS ; PHQ-9
WOS类目	Psychology, Multidisciplinary
WOS研究方向	Psychology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392867
推荐引用方式 GB/T 7714	Lin, David,Nazreen, Tahmida,Rutowski, Tomasz,et al. Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population[J],2022,13.
APA	Lin, David.,Nazreen, Tahmida.,Rutowski, Tomasz.,Lu, Yang.,Harati, Amir.,...&Aratow, Michael.(2022).Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population.FRONTIERS IN PSYCHOLOGY,13.
MLA	Lin, David,et al."Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population".FRONTIERS IN PSYCHOLOGY 13(2022).