ABSTRACT
Mental health is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community. It has intrinsic and instrumental value and is integral to our well-being, and its correlation with environmental factors has been a subject of growing interest. As the pressure of society keeps growing, depression has become a severe problem in modern cities, and finding a way to estimate depression rate is of significance to relieve the problem. In this study, we introduce a Contrastive Language-Image Pretraining (CLIP) based novel approach to predict mental health indicators, especially depression rate, through satellite and street view images. Our methodology uses state-of-the-art Multimodal Large Language Model (MLLM), GPT4-vision, to generate health related captions for satellite and street view images, then we use the generated image-text pairs to fine-tune the CLIP model, making its image encoder extract health related features such as green spaces, sports fields, and infrastructral characteristics. The fine-tuning process is employed to bridge the semantic gap between textual descriptions and visual representations, enabling a comprehensive analysis of geo-tagged images. Consequently, our methodology achieves a notable R2 value of 0.565 on prediction of depression rate in New York City with the combination of satellite and street view images. The successful deployment of Health CLIP in a real-world scenario underscores the practical applicability of our approach.
Supplemental Material
- Amanda J Baxter, George Patton, Kate M Scott, Louisa Degenhardt, and Harvey A Whiteford. 2013. Global epidemiology of mental disorders: what are we missing? PloS one, Vol. 8, 6 (2013), e65514.Google ScholarCross Ref
- Centers for Disease Control and Prevention (CDC). 2013. Web-based Injury Statistics Query and Reporting System (WISQARS). National Center for Injury Prevention and Control, CDC (producer).Google Scholar
- Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005. Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), Vol. 1. IEEE, 539--546.Google Scholar
- Dominic B Dwyer, Peter Falkai, and Nikolaos Koutsouleris. 2018. Machine learning approaches for clinical psychology and psychiatry. Annual review of clinical psychology , Vol. 14 (2018), 91--118.Google Scholar
- Alec Radford et al. 2021. Learning Transferable Visual Models From Natural Language Supervision. CoRR , Vol. abs/2103.00020 (2021). showeprint[arXiv]2103.00020Google Scholar
- HealthData.gov. [n.,d.]. PLACES: Local Data for Better Health - Census Tract Data. https://healthdata.gov/dataset/PLACES-Local-Data-for-Better-Health-Census-Tract-D/jpdw-4rwm/about_data.Google Scholar
- Ronald C Kessler, Wai Tat Chiu, Olga Demler, and Ellen E Walters. 2005. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of general psychiatry, Vol. 62, 6 (2005), 617--627.Google Scholar
- Jihyeon Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David Lobell, and Stefano Ermon. 2021. Predicting livelihood indicators from community-generated street-level imagery. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 268--276.Google ScholarCross Ref
- Fan Liu, Delong Chen, Zhangqingyun Guan, Xiaocong Zhou, Jiale Zhu, and Jun Zhou. 2023. RemoteCLIP: A Vision Language Foundation Model for Remote Sensing. arxiv: 2306.11029 [cs.CV]Google Scholar
- Wei Qin, Zetong Chen, Lei Wang, Yunshi Lan, Weijieying Ren, and Richang Hong. 2023. Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media. arxiv: 2305.05138 [cs.CL]Google Scholar
- Andrew G Reece and Christopher M Danforth. 2017. Instagram photos reveal predictive markers of depression. EPJ Data Science, 6 (15), 1--12.Google Scholar
- Theo et al. Vos. 2016. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990--2015: a systematic analysis for the Global Burden of Disease Study 2015. The lancet, Vol. 388, 10053 (2016), 1545--1602.Google Scholar
- Zhecheng Wang, Haoyuan Li, and Ram Rajagopal. 2020. Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 01 (Apr. 2020), 1013--1020.Google ScholarCross Ref
- World Health Organization. [n.,d.]. Mental health. https://www.who.int/health-topics/mental-health#tab=tab_1Google Scholar
- Xuhai Xu, Bingshen Yao, Yuanzhe Dong, Hong Yu, James Hendler, Anind K Dey, and Dakuo Wang. 2023. Leveraging large language models for mental health prediction via online text data. arXiv preprint arXiv:2307.14385 (2023).Google Scholar
Index Terms
- Health CLIP: Depression Rate Prediction Using Health Related Features in Satellite and Street View Images
Recommendations
Prediction and Analysis of Multiple Causes of Mental Health Problems Based on Machine Learning
Wisdom, Well-Being, Win-WinAbstractTo prevent other types of mental health problems from being misclassified as depression, as well as to remedy the problem of inadequate resources for mental health consultations. This study first analyzes the types of different causes of mental ...
Role of social media in tackling challenges in mental health
SAM '13: Proceedings of the 2nd international workshop on Socially-aware multimediaMental illness is a serious and widespread health challenge in our society today. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. This position paper highlights some recent attempts examining ...
Can social media help us reason about mental health?
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebMillions of people each year suffer from depression, which makes mental illness one of the most serious and widespread health challenges in our society today. There is therefore a need for effective policies, interventions, and prevention strategies ...
Comments