{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:24:19Z","timestamp":1771237459199,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2015,8,10]],"date-time":"2015-08-10T00:00:00Z","timestamp":1439164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2015,8,10]]},"DOI":"10.1145\/2783258.2783352","type":"proceedings-article","created":{"date-parts":[[2015,8,7]],"date-time":"2015-08-07T15:38:27Z","timestamp":1438961907000},"page":"705-714","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":110,"title":["Temporal Phenotyping from Longitudinal Electronic Health Records"],"prefix":"10.1145","author":[{"given":"Chuanren","family":"Liu","sequence":"first","affiliation":[{"name":"Drexel University, Philadelphia, USA"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Connecticut, Storrs, USA"}]},{"given":"Jianying","family":"Hu","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center, Yorktown Heights, USA"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, Newark, USA"}]}],"member":"320","published-online":{"date-parts":[[2015,8,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"MIT Technology Review Business Report, 117 (5): 1--19","author":"Data","year":"2014","unstructured":"Data driven healthcare. MIT Technology Review Business Report, 117 (5): 1--19 , 2014 . Data driven healthcare. MIT Technology Review Business Report, 117 (5): 1--19, 2014."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.114"},{"key":"e_1_3_2_1_3_1","volume-title":"Arrhythmia in heart failure: role of mechanically induced changes in electrophysiology. The Lancet, 333 (8650)","author":"Dean W","year":"1989","unstructured":"John W Dean and Max J Lab . Arrhythmia in heart failure: role of mechanically induced changes in electrophysiology. The Lancet, 333 (8650) , 1989 . JohnW Dean and MaxJ Lab. Arrhythmia in heart failure: role of mechanically induced changes in electrophysiology. The Lancet, 333 (8650), 1989."},{"key":"e_1_3_2_1_4_1","volume-title":"A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. Journal of biomedical informatics, 48","author":"Gotz David","year":"2014","unstructured":"David Gotz , Fei Wang , and Adam Perer . A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. Journal of biomedical informatics, 48 , 2014 . David Gotz, Fei Wang, and Adam Perer. A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. Journal of biomedical informatics, 48, 2014."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.07.001"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623658"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-001145"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3208"},{"key":"e_1_3_2_1_9_1","volume-title":"Workshop DMMI in AMIA","author":"Kale David","year":"2014","unstructured":"David Kale , Zhengping Che , and Yan Liu . Computational discovery of physiomes in critically ill children using deep learning . Workshop DMMI in AMIA , 2014 . David Kale, Zhengping Che, and Yan Liu. Computational discovery of physiomes in critically ill children using deep learning. Workshop DMMI in AMIA, 2014."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375680"},{"key":"e_1_3_2_1_11_1","volume-title":"Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3 (3): 263--286","author":"Keogh Eamonn","year":"2001","unstructured":"Keogh, Chakrabarti, Pazzani, and Mehrotra}keogh2001dimensionality Eamonn Keogh , Kaushik Chakrabarti , Michael Pazzani , and Sharad Mehrotra . Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3 (3): 263--286 , 2001 \\natexlabb. Keogh, Chakrabarti, Pazzani, and Mehrotra}keogh2001dimensionalityEamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3 (3): 263--286, 2001\\natexlabb."},{"key":"e_1_3_2_1_12_1","volume-title":"ICDM","author":"Keogh Eamonn","unstructured":"Keogh, Chu, Hart, and Pazzani}keogh2001online Eamonn Keogh , Selina Chu , David Hart , and Michael Pazzani . An online algorithm for segmenting time series . ICDM , 2001\\natexlabc. Keogh, Chu, Hart, and Pazzani}keogh2001onlineEamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. An online algorithm for segmenting time series. ICDM, 2001\\natexlabc."},{"key":"e_1_3_2_1_13_1","volume-title":"Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 8 (6): e66341","author":"Lasko Thomas A","year":"2013","unstructured":"Thomas A Lasko , Joshua C Denny , and Mia A Levy . Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 8 (6): e66341 , 2013 . Thomas A Lasko, Joshua C Denny, and Mia A Levy. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 8 (6): e66341, 2013."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1996.03530440037034"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.10.2756"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/882082.882086"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623741"},{"key":"e_1_3_2_1_18_1","volume-title":"Data-Driven Healthcare: How Analytics and BI are Transforming the Industry","author":"Madsen Laura B.","year":"2014","unstructured":"Laura B. Madsen . Data-Driven Healthcare: How Analytics and BI are Transforming the Industry . Wiley , 2014 . Laura B. Madsen. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry. Wiley, 2014."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972801.28"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-007-0070-1"},{"key":"e_1_3_2_1_21_1","volume-title":"AMIA","author":"Moskovitch Robert","year":"2009","unstructured":"Robert Moskovitch and Yuval Shahar . Medical temporal-knowledge discovery via temporal abstraction . AMIA , 2009 . Robert Moskovitch and Yuval Shahar. Medical temporal-knowledge discovery via temporal abstraction. AMIA, 2009."},{"key":"e_1_3_2_1_22_1","volume-title":"Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. Journal of the American Medical Informatics Association, 20 (e2): e206--e211","author":"Pathak Jyotishman","year":"2013","unstructured":"Jyotishman Pathak , Abel N Kho , and Joshua C Denny . Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. Journal of the American Medical Informatics Association, 20 (e2): e206--e211 , 2013 . Jyotishman Pathak, Abel N Kho, and Joshua C Denny. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. Journal of the American Medical Informatics Association, 20 (e2): e206--e211, 2013."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2557500.2557508"},{"key":"e_1_3_2_1_24_1","volume-title":"Knowledge-based temporal abstraction in clinical domains. Artificial intelligence in medicine, 8 (3): 267--298","author":"Shahar Yuval","year":"1996","unstructured":"Yuval Shahar and Mark A Musen . Knowledge-based temporal abstraction in clinical domains. Artificial intelligence in medicine, 8 (3): 267--298 , 1996 . Yuval Shahar and Mark A Musen. Knowledge-based temporal abstraction in clinical domains. Artificial intelligence in medicine, 8 (3): 267--298, 1996."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2006.08.002"},{"key":"e_1_3_2_1_26_1","volume-title":"Fei Wang. Workshop at amia on data mining for medical informatics: Electronic phenotyping.","author":"Stiglic Gregor","year":"2014","unstructured":"Gregor Stiglic , Nigam H. Shah , Niels Peek , and Fei Wang. Workshop at amia on data mining for medical informatics: Electronic phenotyping. Nov 15, 2014 . Gregor Stiglic, Nigam H. Shah, Niels Peek, and Fei Wang. Workshop at amia on data mining for medical informatics: Electronic phenotyping. Nov 15, 2014."},{"key":"e_1_3_2_1_27_1","volume-title":"metabolism, and the complex pathophysiology of cachexia in chronic heart failure. Cardiovascular research, 73 (2): 298--309","author":"von Haehling Stephan","year":"2007","unstructured":"Stephan von Haehling , Wolfram Doehner , and Stefan D Anker . Nutrition , metabolism, and the complex pathophysiology of cachexia in chronic heart failure. Cardiovascular research, 73 (2): 298--309 , 2007 . Stephan von Haehling, Wolfram Doehner, and Stefan D Anker. Nutrition, metabolism, and the complex pathophysiology of cachexia in chronic heart failure. Cardiovascular research, 73 (2): 298--309, 2007."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.111"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623711"}],"event":{"name":"KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Sydney NSW Australia","acronym":"KDD '15","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2783258.2783352","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2783258.2783352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:16:57Z","timestamp":1750227417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2783258.2783352"}},"subtitle":["A Graph Based Framework"],"short-title":[],"issued":{"date-parts":[[2015,8,10]]},"references-count":29,"alternative-id":["10.1145\/2783258.2783352","10.1145\/2783258"],"URL":"https:\/\/doi.org\/10.1145\/2783258.2783352","relation":{},"subject":[],"published":{"date-parts":[[2015,8,10]]},"assertion":[{"value":"2015-08-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}