Editorial: Quantitative modeling of psychopathology using passively collected data

Frontiers in Psychology

article

Nicholas C. Jacobson¹  Burkhardt Funk²  Saeed Abdullah³ 

[1] Center for Technology and Behavioral Health, Departments of Biomedical Data Science, Psychiatry, and Computer Science, Geisel School of Medicine, Dartmouth College;Leuphana University Lüneburg;College of Information Sciences and Technology, Penn State University

关键词: Digital phenotyping;    digital biomarker;    Mobile sensing;    machine learning;    passive sensing;   

DOI  :  10.3389/fpsyg.2022.1126971

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Traditional mental health assessment collects data at a single point in time, which does not provide a comprehensive picture of an individual's mental health over an extended period. To address this issue, methods have been developed that involve repeatedly asking people to fill out surveys. However, these methods can be burdensome and as such, might not support granular assessment. Advances in technology have made it possible to collect a wide range of behavioral, social, and physiological data about an individual without requiring their active participation. This data can be collected from various sources, including smartphone and wearable sensors, electronic health records, social media data, and internet search data, with minimal burden to the individual. As a result, this data can provide a more comprehensive and nuanced understanding of an individual's mental health over time.

【 授权许可】

CC BY