Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression
Abstract
Depression is a major public health issue with direct and significant effects on both physical and mental health. In this study, we analyze smartphone sensing data to find differential behavioral features that are correlated with depression measures such as patient health questionnaire (PHQ-9). Our approach uses an innovative multi-view bi-clustering algorithm. It takes multiple views of sensing data as input to identify homogeneous behavioral groups and simultaneously the key sensing features that characterize the different groups. Using a publicly available dataset, we discover that these behavioral groups with differential sensing features are highly discriminative of PHQ-9 scores that are self reported by the study subjects. For instance, the group comprising less active users in the sensed activities corresponds to overall higher PHQ-9 scores. We then employ the key sensing features that distinguish the different groups to create predictive models to predict the group assignment of individuals. We verify the generalizability of these models using the support vector machine classifier. Cross validation studies show that our classifiers can classify individuals into the correct subgroups with an overall accuracy of 87%.
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Predictive modeling of mental health using digital biomarkers is possible in a more general population, not only in small homogenous populations.
“These findings show that predictive modeling of mental health using digital biomarkers is not only possible in small homogenous populations [83,84], but also in a more general population”
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“The clusters were similar to the one generated in [81].”
MethodResult
- Authors
- Farhan, A. A., Lu, J., Bi, J., Russell, A., Wang, B., & Bamis, A.
- Affiliations
- University of Connecticut (US)
- Venue
- 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 264-273
- ISSN
- 2832-2967 (ISSN-L)
- OpenAlex
- W2517889164
- Cited by
- 61
- References
- 28
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 12 · 2021: 16 · 2020: 7 · 2019: 4 · 2018: 5 · 2017: 2
- FWCI
- 1.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- depression detection, smartphone sensing, PHQ-9, behavioral characteristics, physical activity, group classification, mobile health, support vector machine
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