{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:18:26Z","timestamp":1765484306735,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2019-06216"],"award-info":[{"award-number":["RGPIN-2019-06216"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010785","name":"Canada First Research Excellence Fund","doi-asserted-by":"publisher","award":["G249591"],"award-info":[{"award-number":["G249591"]}],"id":[{"id":"10.13039\/501100010785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3542675","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"4713-4723","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Automatic Phenotyping by a Seed-guided Topic Model"],"prefix":"10.1145","author":[{"given":"Ziyang","family":"Song","sequence":"first","affiliation":[{"name":"McGill University, Montreal, PQ, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyi","family":"Hu","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, PQ, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aman","family":"Verma","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, PQ, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David L.","family":"Buckeridge","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, PQ, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, PQ, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1101\/2021.12.17.473215"},{"key":"e_1_3_2_1_3_1","volume-title":"Monitoring chronic diseases in Canada: the Chronic Disease Indicator Framework. Chronic diseases and injuries in Canada 34 Suppl 1","author":"Betancourt M T","year":"2014","unstructured":"M T Betancourt, K C Roberts, T-L Bennett, E R Driscoll, G Jayaraman, and L Pelletier. 2014. Monitoring chronic diseases in Canada: the Chronic Disease Indicator Framework. Chronic diseases and injuries in Canada 34 Suppl 1 (2014), 1--30."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143859"},{"volume-title":"Proceedings of the 20th International Conference on Neural Information Processing Systems","author":"David","key":"e_1_3_2_1_6_1","unstructured":"David M. Blei and Jon D. McAuliffe. 2007. Supervised Topic Models. In Proceedings of the 20th International Conference on Neural Information Processing Systems (Vancouver, British Columbia, Canada) (NIPS'07). Curran Associates Inc., Red Hook, NY, USA, 121--128."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Eliezer de Souza da Silva Helge Langseth and Heri Ramampiaro. 2017. Content-Based Social Recommendation with Poisson Matrix Factorization. In ECML\/PKDD.","DOI":"10.1007\/978-3-319-71249-9_32"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Joshua C Denny Lisa Bastarache Marylyn D Ritchie Robert J Carroll Raquel Zink Jonathan D Mosley Julie R Field Jill M Pulley Andrea H Ramirez Erica Bowton et al. 2013. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature biotechnology 31 12 (2013) 1102--1111.","DOI":"10.1038\/nbt.2749"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq126"},{"key":"e_1_3_2_1_11_1","volume-title":"Blei","author":"Dieng Adji B.","year":"2019","unstructured":"Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. 2019. The Dynamic Embedded Topic Model. arXiv:1907.05545 [cs.CL]"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep40841"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/2567709.2502622"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-001145"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2380816.2380844"},{"key":"e_1_3_2_1_17_1","unstructured":"Diederik P Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114 [stat.ML]"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","unstructured":"Yue Li Pratheeksha Nair Xing Han Lu Zhi Wen Yuening Wang Amir Dehaghi Yan Miao Weiqi Liu Tamas Ordog Joanna Biernacka Euijung Ryu Janet Olson Mark Frye Aihua Liu Liming Guo Ariane Marelli Yuri Ahuja Jose Davila- Velderrain and Manolis Kellis. 2020. Inferring multimodal latent topics from electronic health records. Nature Communications 11 (05 2020) 2536. https: \/\/doi.org\/10.1038\/s41467-020-16378-3","DOI":"10.1038\/s41467-020-16378-3"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h1885"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-"},{"key":"e_1_3_2_1_21_1","volume-title":"Hospitals' Use of Electronic Health Records Data","author":"Parasrampuria Sonal","year":"2015","unstructured":"Sonal Parasrampuria and Jawanna Henry. 2019. Hospitals' Use of Electronic Health Records Data, 2015--2017."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2015.10.001"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","unstructured":"Arash Shaban-Nejad Maxime Lavigne Anya Okhmatovskaia and David Buckeridge. 2016. PopHR: a knowledge-based platform to support integration analysis and visualization of population health data: The Population Health Record (PopHR). Annals of the New York Academy of Sciences 1387 (10 2016). https:\/\/doi.org\/10.1111\/nyas.13271","DOI":"10.1111\/nyas.13271"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459930.3469543"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the 19th International Conference on Neural Information Processing Systems (Canada) (NIPS'06)","author":"Teh Yee Whye","year":"2006","unstructured":"Yee Whye Teh, David Newman, and Max Welling. 2006. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. In Proceedings of the 19th International Conference on Neural Information Processing Systems (Canada) (NIPS'06). MIT Press, Cambridge, MA, USA, 1353--1360."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103364"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-015-0166-y"},{"key":"e_1_3_2_1_28_1","volume-title":"Annual Symposium proceedings. AMIA Symposium 2017 (04 2018)","author":"Yuan Mengru","year":"2018","unstructured":"Mengru Yuan, Guido Powell, Maxime Lavigne, Anya Okhmatovskaia, and David Buckeridge. 2018. Initial Usability Evaluation of a Knowledge-Based Population Health Information System: The Population Health Record (PopHR). AMIA... Annual Symposium proceedings. AMIA Symposium 2017 (04 2018), 1878--1884."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Washington DC USA","acronym":"KDD '22"},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542675","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3542675","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:53Z","timestamp":1750186973000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":28,"alternative-id":["10.1145\/3534678.3542675","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3542675","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}