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We extract 16 mobility features from these passively collected mobility traces and train an XGBoost model to predict users' hospital visits. We demonstrate that the designed mobility features can significantly improve prediction accuracy (p &lt; 0.01, AUC = 0.79). We further analyze how these mobility features affect the prediction results and measure their importance by using Shapley additive explanation values. We discover that users with less mobility activity, less visit diversity, and few sports facilities, bountiful entertainment around their visited locations are more likely to visit hospitals. Moreover, we conduct predictions on the populations with different demographic features, which achieves meaningful and insightful results, i.e. maintaining a high mobility activity is crucial for older people's health, while fast food store more substantially affects younger people's health; visit patterns can indicate females' health, while the neighborhood environment is more indicative of males, etc. These results shed light on how to use and understand large scale mobility data in health monitoring and other health-related applications in practice.<\/jats:p>","DOI":"10.1145\/3448078","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:56:41Z","timestamp":1617130601000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Passive Health Monitoring Using Large Scale Mobility Data"],"prefix":"10.1145","volume":"5","author":[{"given":"Yunke","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengli","family":"Xu","sequence":"additional","affiliation":[{"name":"CSE, Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[{"name":"CSE, Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassilis","family":"Kostakos","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"CSE, Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature23018"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/532020a"},{"key":"e_1_2_2_3_1","volume-title":"United States: A systematic review of study characteristics. 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