{"type":"ScholarlyArticle","title":"Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data","authors":[{"name":"Yingzi Wang","slug":"yingzi-wang","given":"Yingzi","family":"Wang","affiliation":"University of Science and Technology of China; University of Cambridge"},{"name":"Xiao Zhou","slug":"xiao-zhou","given":"Xiao","family":"Zhou","affiliation":"University of Cambridge"},{"name":"Anastasios Noulas","slug":"anastasios-noulas","given":"Anastasios","family":"Noulas","affiliation":"Center for Data Science of New York University"},{"name":"Cecilia Mascolo","slug":"cecilia-mascolo","given":"Cecilia","family":"Mascolo","affiliation":"University of Cambridge"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"University of Science and Technology of China"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"University of Science and Technology of China"}],"abstract":"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.","doi":"10.24963/ijcai.2018/497","url":"https://ijcai.org/proceedings/2018/497","pdf":"https://ijcai.org/proceedings/2018/0497.pdf","markdown":"https://ijcai.org/proceedings/2018/497.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/497","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":497,"pages":{"first":3578,"last":3584},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning Applications: Bio;Medicine"],"videos":[],"references":[{"position":1,"raw":"[Bilmes and others, 1998] Jeff A Bilmes et al. 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