Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data
Open accessAbstract
Chronic diseases like cancer and diabetes are major threats to human life. Understanding the distribution and progression of chronic diseases of a population is important in assisting the allocation of medical resources as well as the design of policies in preemptive healthcare. Traditional methods to obtain large scale indicators on population health, e.g., surveys and statistical analysis, can be costly and time-consuming and often lead to a coarse spatio-temporal picture. In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area. By viewing local human lifestyles we predict the evolution rate of several chronic diseases at the level of a city neighborhood. We apply the combination of a collaborative topic modeling (CTM) and a Gaussian mixture method (GMM) to tackle the data sparsity challenge and achieve robust predictions on health conditions simultaneously. Our method enables the analysis and prediction of disease rate evolution at fine spatio-temporal scales and demonstrates the potential of incorporating datasets from mobile web sources to improve population health monitoring. Evaluations using real-world check-in and chronic disease morbidity datasets in the city of London show that the proposed CTM+GMM model outperforms various baseline methods.
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Navigation lifestyle analysis is helpful in studying longitudinal disease progression.
“Apart from diagnostic inference at snapshots, navigation lifestyle analysis is helpful in studying longitudinal disease progression [118].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [118]
In a city region the distribution of POI categories is a good predictor of the distribution of progression of chronic diseases.
“The main insight here is that in a city region the distribution of POI categories is a good predictor of the distribution of progression of chronic diseases.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [118]
The study exploits the causal relationship between chronic diseases and daily habits captured by check-ins.
“The study exploits the causal relationship between chronic diseases and daily habits captured by check-ins.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [118]
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- Authors
- Wang, Y., Zhou, X., Noulas, A., Mascolo, C., Xie, X., & Chen, E.
- Affiliations
- University of Science and Technology of China (CN)
- University of Cambridge (GB)
- New York University (US)
- University of Cambridge
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 3578-3584
- OpenAlex
- W4233723981
- Cited by
- 22
- References
- 16
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 4
- FWCI
- 6.85
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- University of Cambridge
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- chronic disease prediction, human mobility, Gaussian mixture model, data sparsity, population health monitoring, check-in data, cancer, diabetes mellitus
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