{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:52:28Z","timestamp":1761490348099,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,8,13]],"date-time":"2017-08-13T00:00:00Z","timestamp":1502582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["NRF-CRP8-2011-08"],"award-info":[{"award-number":["NRF-CRP8-2011-08"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,8,13]]},"DOI":"10.1145\/3097983.3098149","type":"proceedings-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T18:35:54Z","timestamp":1501871754000},"page":"2171-2180","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Resolving the Bias in Electronic Medical Records"],"prefix":"10.1145","author":[{"given":"Kaiping","family":"Zheng","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Jinyang","family":"Gao","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Kee Yuan","family":"Ngiam","sequence":"additional","affiliation":[{"name":"National University Health System, Singapore, Singapore"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Wei Luen James","family":"Yip","sequence":"additional","affiliation":[{"name":"National University Health System, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2017,8,13]]},"reference":[{"volume-title":"Variational algorithms for approximate Bayesian inference","author":"Beal Matthew James","unstructured":"Matthew James Beal . 2003. Variational algorithms for approximate Bayesian inference . University of London United Kingdom. Matthew James Beal. 2003. Variational algorithms for approximate Bayesian inference. University of London United Kingdom.","key":"e_1_3_2_1_1_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1007\/978-1-4615-5529-2_5"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1145\/2783258.2783365"},{"key":"e_1_3_2_1_4_1","volume-title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values. arXiv preprint arXiv:1606.01865","author":"Che Zhengping","year":"2016","unstructured":"Zhengping Che , Sanjay Purushotham , Kyunghyun Cho , David Sontag , and Yan Liu 2016. Recurrent Neural Networks for Multivariate Time Series with Missing Values. arXiv preprint arXiv:1606.01865 ( 2016 ). Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu 2016. Recurrent Neural Networks for Multivariate Time Series with Missing Values. arXiv preprint arXiv:1606.01865 (2016)."},{"key":"e_1_3_2_1_5_1","volume-title":"Distilling Knowledge from Deep Networks with Applications to Healthcare Domain. arXiv preprint arXiv:1512.03542","author":"Che Zhengping","year":"2015","unstructured":"Zhengping Che , Sanjay Purushotham , Robinder Khemani , and Yan Liu 2015. Distilling Knowledge from Deep Networks with Applications to Healthcare Domain. arXiv preprint arXiv:1512.03542 ( 2015 ). Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu 2015. Distilling Knowledge from Deep Networks with Applications to Healthcare Domain. arXiv preprint arXiv:1512.03542 (2015)."},{"key":"e_1_3_2_1_6_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart Van Merri\u00ebnboer , Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_1_7_1","series-title":"Series B (methodological)","volume-title":"Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society","author":"Dempster Arthur P","year":"1977","unstructured":"Arthur P Dempster , Nan M Laird , and Donald B Rubin . 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society . Series B (methodological) ( 1977 ), 1--38. Arthur P Dempster, Nan M Laird, and Donald B Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological) (1977), 1--38."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1016\/j.neuroimage.2009.04.023"},{"volume-title":"Deep Learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press . shownotehttp:\/\/www.deeplearningbook.org. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. shownotehttp:\/\/www.deeplearningbook.org.","key":"e_1_3_2_1_9_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_11_1","volume-title":"Parameterizing time in electronic health record studies. Journal of the American Medical Informatics Association","author":"Hripcsak George","year":"2015","unstructured":"George Hripcsak , David J Albers , and Adler Perotte . 2015. Parameterizing time in electronic health record studies. Journal of the American Medical Informatics Association ( 2015 ), ocu051. George Hripcsak, David J Albers, and Adler Perotte. 2015. Parameterizing time in electronic health record studies. Journal of the American Medical Informatics Association (2015), ocu051."},{"key":"e_1_3_2_1_12_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson , Tom J Pollard , Lu Shen , Li-wei H Lehman , Mengling Feng , Mohammad Ghassemi , Benjamin Moody , Peter Szolovits , Leo Anthony Celi, and Roger G Mark . 2016 . MIMIC-III, a freely accessible critical care database. Scientific data Vol. 3 (2016). Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data Vol. 3 (2016)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Learning Representations","author":"Lipton Zachary C","year":"2016","unstructured":"Zachary C Lipton , David C Kale , Charles Elkan , and Randall Wetzell 2016 n atexlabb. Learning to diagnose with LSTM recurrent neural networks . International Conference on Learning Representations (2016). Zachary C Lipton, David C Kale, Charles Elkan, and Randall Wetzell 2016natexlabb. Learning to diagnose with LSTM recurrent neural networks. International Conference on Learning Representations (2016)."},{"unstructured":"Zachary C Lipton David C Kale and Randall Wetzel. 2016natexlaba. Modeling Missing Data in Clinical Time Series with RNNs Machine Learning for Healthcare.  Zachary C Lipton David C Kale and Randall Wetzel. 2016natexlaba. Modeling Missing Data in Clinical Time Series with RNNs Machine Learning for Healthcare.","key":"e_1_3_2_1_15_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1145\/2733373.2806217"},{"key":"e_1_3_2_1_17_1","volume-title":"DeepCare: A Deep Dynamic Memory Model for Predictive Medicine Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 30--41","author":"Pham Trang","year":"2016","unstructured":"Trang Pham , Truyen Tran , Dinh Phung , and Svetha Venkatesh . 2016 . DeepCare: A Deep Dynamic Memory Model for Predictive Medicine Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 30--41 . Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. 2016. DeepCare: A Deep Dynamic Memory Model for Predictive Medicine Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 30--41."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1016\/j.jbi.2014.03.016"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1109\/5.18626"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.2337\/dc06-2089"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.1016\/j.neuroimage.2010.03.051"},{"key":"e_1_3_2_1_22_1","volume-title":"Unsupervised Learning of Disease Progression Models Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14)","author":"Wang Xiang","year":"2014","unstructured":"Xiang Wang , David Sontag , and Fei Wang 2014 . Unsupervised Learning of Disease Progression Models Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14) . ACM, New York, NY, USA, 85--94. Xiang Wang, David Sontag, and Fei Wang 2014. Unsupervised Learning of Disease Progression Models Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14). ACM, New York, NY, USA, 85--94."},{"volume-title":"Modeling Disease Progression via Fused Sparse Group Lasso Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '12)","author":"Zhou Jiayu","unstructured":"Jiayu Zhou , Jun Liu , Vaibhav A. Narayan , and Jieping Ye. 2012. Modeling Disease Progression via Fused Sparse Group Lasso Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '12) . ACM , New York, NY, USA , 1095--1103. Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. 2012. Modeling Disease Progression via Fused Sparse Group Lasso Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '12). ACM, New York, NY, USA, 1095--1103.","key":"e_1_3_2_1_23_1"},{"volume-title":"A Multi-task Learning Formulation for Predicting Disease Progression Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11)","author":"Zhou Jiayu","unstructured":"Jiayu Zhou , Lei Yuan , Jun Liu , and Jieping Ye. 2011. A Multi-task Learning Formulation for Predicting Disease Progression Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11) . ACM , New York, NY, USA , 814--822. endthebibliography Jiayu Zhou, Lei Yuan, Jun Liu, and Jieping Ye. 2011. A Multi-task Learning Formulation for Predicting Disease Progression Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11). ACM, New York, NY, USA, 814--822. endthebibliography","key":"e_1_3_2_1_24_1"}],"event":{"sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"acronym":"KDD '17","name":"KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Halifax NS Canada"},"container-title":["Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098149","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3097983.3098149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:30:02Z","timestamp":1750217402000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,13]]},"references-count":24,"alternative-id":["10.1145\/3097983.3098149","10.1145\/3097983"],"URL":"https:\/\/doi.org\/10.1145\/3097983.3098149","relation":{},"subject":[],"published":{"date-parts":[[2017,8,13]]},"assertion":[{"value":"2017-08-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}