{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T15:53:53Z","timestamp":1725810833149},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,2]]},"DOI":"10.1145\/3368555.3384457","type":"proceedings-article","created":{"date-parts":[[2020,3,20]],"date-time":"2020-03-20T20:37:37Z","timestamp":1584736657000},"page":"204-213","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Analyzing the role of model uncertainty for electronic health records"],"prefix":"10.1145","volume":"1706","author":[{"given":"Michael W.","family":"Dusenberry","sequence":"first","affiliation":[{"name":"Google"}]},{"given":"Dustin","family":"Tran","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Edward","family":"Choi","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Jonas","family":"Kemp","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Jeremy","family":"Nixon","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Ghassen","family":"Jerfel","sequence":"additional","affiliation":[{"name":"Google, Duke University"}]},{"given":"Katherine","family":"Heller","sequence":"additional","affiliation":[{"name":"Google"}]},{"given":"Andrew M.","family":"Dai","sequence":"additional","affiliation":[{"name":"Google"}]}],"member":"320","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","year":"2016","author":"Abadi Mart\u00edn","key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . Tensorflow: A system for large-scale machine learning . In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) . 265--283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 265--283."},{"volume-title":"Pattern Recognition and Machine Learning (corrected 8th printing 2009 ed.)","author":"Bishop Christopher M.","key":"e_1_3_2_1_2_1","unstructured":"Christopher M. Bishop . 2006. Pattern Recognition and Machine Learning (corrected 8th printing 2009 ed.) . Springer , New York . Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (corrected 8th printing 2009 ed.). Springer, New York."},{"volume-title":"Weight Uncertainty in Neural Networks. arXiv.org (May","year":"2015","author":"Blundell Charles","key":"e_1_3_2_1_3_1","unstructured":"Charles Blundell , Julien Cornebise , Koray Kavukcuoglu , and Daan Wierstra . 2015. Weight Uncertainty in Neural Networks. arXiv.org (May 2015 ). http:\/\/arxiv.org\/abs\/1505.05424v2 Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight Uncertainty in Neural Networks. arXiv.org (May 2015). http:\/\/arxiv.org\/abs\/1505.05424v2"},{"volume-title":"Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In Advances in Neural Information Processing Systems. 4547--4557.","year":"2018","author":"Choi Edward","key":"e_1_3_2_1_4_1","unstructured":"Edward Choi , Cao Xiao , Walter Stewart , and Jimeng Sun . 2018 . Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In Advances in Neural Information Processing Systems. 4547--4557. Edward Choi, Cao Xiao, Walter Stewart, and Jimeng Sun. 2018. Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In Advances in Neural Information Processing Systems. 4547--4557."},{"key":"e_1_3_2_1_5_1","unstructured":"National Research Council et al. 2011. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. National Academies Press. National Research Council et al. 2011. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease . National Academies Press."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajem.2016.10.065"},{"volume-title":"Bayesian Recurrent Neural Networks. arXiv.org (April","year":"2017","author":"Fortunato Meire","key":"e_1_3_2_1_7_1","unstructured":"Meire Fortunato , Charles Blundell , and Oriol Vinyals . 2017. Bayesian Recurrent Neural Networks. arXiv.org (April 2017 ). http:\/\/arxiv.org\/abs\/1704.02798v3 Meire Fortunato, Charles Blundell, and Oriol Vinyals. 2017. Bayesian Recurrent Neural Networks. arXiv.org (April 2017). http:\/\/arxiv.org\/abs\/1704.02798v3"},{"volume-title":"international conference on machine learning. 1050--1059","year":"2016","author":"Gal Yarin","key":"e_1_3_2_1_8_1","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. 1050--1059."},{"volume-title":"Neural processes. arXiv preprint arXiv:1807.01622","year":"2018","author":"Garnelo Marta","key":"e_1_3_2_1_9_1","unstructured":"Marta Garnelo , Jonathan Schwarz , Dan Rosenbaum , Fabio Viola , Danilo J Rezende , SM Eslami , and Yee Whye Teh . 2018. Neural processes. arXiv preprint arXiv:1807.01622 ( 2018 ). Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh. 2018. Neural processes. arXiv preprint arXiv:1807.01622 (2018)."},{"volume-title":"Medical ethics: four principles plus attention to scope. Bmj 309, 6948","year":"1994","author":"Gillon Raanan","key":"e_1_3_2_1_10_1","unstructured":"Raanan Gillon . 1994. Medical ethics: four principles plus attention to scope. Bmj 309, 6948 ( 1994 ), 184. Raanan Gillon. 1994. Medical ethics: four principles plus attention to scope. Bmj 309, 6948 (1994), 184."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_1_12_1","volume-title":"On Calibration of Modern Neural Networks. In International Conference on Machine Learning (ICML)","volume":"1706","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo , Geoff Pleiss , Yu Sun , and Kilian Q Weinberger . 2017 . On Calibration of Modern Neural Networks. In International Conference on Machine Learning (ICML) , Vol. cs.LG. Cornell University Library. http:\/\/arxiv.org\/abs\/ 1706 .04599v2 Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On Calibration of Modern Neural Networks. In International Conference on Machine Learning (ICML), Vol. cs.LG. Cornell University Library. http:\/\/arxiv.org\/abs\/1706.04599v2"},{"volume-title":"Reliable uncertainty estimates in deep neural networks using noise contrastive priors. arXiv preprint arXiv:1807.09289","year":"2018","author":"Hafner Danijar","key":"e_1_3_2_1_13_1","unstructured":"Danijar Hafner , Dustin Tran , Alex Irpan , Timothy Lillicrap , and James Davidson . 2018. Reliable uncertainty estimates in deep neural networks using noise contrastive priors. arXiv preprint arXiv:1807.09289 ( 2018 ). Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, and James Davidson. 2018. Reliable uncertainty estimates in deep neural networks using noise contrastive priors. arXiv preprint arXiv:1807.09289 (2018)."},{"volume-title":"Greg Ver Steeg, and Aram Galstyan","year":"2017","author":"Harutyunyan Hrayr","key":"e_1_3_2_1_14_1","unstructured":"Hrayr Harutyunyan , Hrant Khachatrian , David C Kale , Greg Ver Steeg, and Aram Galstyan . 2017 . Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771 (2017). Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan. 2017. Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771 (2017)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"e_1_3_2_1_16_1","unstructured":"Alex Kendall and Yarin Gal. 2017. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?. In Advances in Neural Information Processing Systems. http:\/\/arxiv.org\/abs\/1703.04977 Alex Kendall and Yarin Gal. 2017. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?. In Advances in Neural Information Processing Systems . http:\/\/arxiv.org\/abs\/1703.04977"},{"volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","year":"2014","author":"Kingma Diederik P","key":"e_1_3_2_1_17_1","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"volume-title":"Uncertainty and Profit","author":"Knight Frank H.","key":"e_1_3_2_1_18_1","unstructured":"Frank H. Knight . 1957. Risk , Uncertainty and Profit . New York , Kelley & Millman . https:\/\/mises.org\/sites\/default\/files\/Risk,%20Uncertainty,%20and%20Profit_4.pdf Frank H. Knight. 1957. Risk, Uncertainty and Profit. New York, Kelley & Millman. https:\/\/mises.org\/sites\/default\/files\/Risk,%20Uncertainty,%20and%20Profit_4.pdf"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3122009.3122023"},{"volume-title":"Advances in Neural Information Processing Systems","author":"Lakshminarayanan Balaji","key":"e_1_3_2_1_20_1","unstructured":"Balaji Lakshminarayanan , Alexander Pritzel , and Charles Blundell . 2017. Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles . In Advances in Neural Information Processing Systems , Vol. stat.ML. http:\/\/arxiv.org\/abs\/ 1612 .01474v3 Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles. In Advances in Neural Information Processing Systems, Vol. stat.ML. http:\/\/arxiv.org\/abs\/1612.01474v3"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305910"},{"key":"e_1_3_2_1_22_1","unstructured":"Andrey Malinin and Mark Gales. 2018. Predictive uncertainty estimation via prior networks. In Advances in Neural Information Processing Systems. 7047--7058. Andrey Malinin and Mark Gales. 2018. Predictive uncertainty estimation via prior networks. In Advances in Neural Information Processing Systems . 7047--7058."},{"key":"e_1_3_2_1_23_1","volume-title":"Obtaining Well Calibrated Probabilities Using Bayesian Binning. In AAAI Conference on Artificial Intelligence","volume":"2015","author":"Naeini Mahdi Pakdaman","year":"2015","unstructured":"Mahdi Pakdaman Naeini , Gregory F. Cooper , and Milos Hauskrecht . 2015 . Obtaining Well Calibrated Probabilities Using Bayesian Binning. In AAAI Conference on Artificial Intelligence , Vol. 2015 . 2901--2907. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4410090\/pdf\/nihms679964.pdf Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht. 2015. Obtaining Well Calibrated Probabilities Using Bayesian Binning. In AAAI Conference on Artificial Intelligence, Vol. 2015. 2901--2907. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4410090\/pdf\/nihms679964.pdf"},{"volume-title":"Measuring Calibration in Deep Learning. arXiv:1904.01685 [cs, stat] (April","year":"2019","author":"Nixon Jeremy","key":"e_1_3_2_1_24_1","unstructured":"Jeremy Nixon , Mike Dusenberry , Linchuan Zhang , Ghassen Jerfel , and Dustin Tran . 2019. Measuring Calibration in Deep Learning. arXiv:1904.01685 [cs, stat] (April 2019 ). http:\/\/arxiv.org\/abs\/1904.01685 Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran. 2019. Measuring Calibration in Deep Learning. arXiv:1904.01685 [cs, stat] (April 2019). http:\/\/arxiv.org\/abs\/1904.01685"},{"volume-title":"Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi.","year":"2018","author":"Pollard Tom J","key":"e_1_3_2_1_25_1","unstructured":"Tom J Pollard , Alistair EW Johnson , Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi. 2018 . The eICU Collaborative Research Database , a freely available multi-center database for critical care research. Scientific data 5 (2018). Tom J Pollard, Alistair EW Johnson, Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi. 2018. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific data 5 (2018)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2012.12.006"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.294.12.1519"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.294.12.1511"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(00)04561-X"},{"volume-title":"Bayesian Layers: A Module for Neural Network Uncertainty. arXiv:1812.03973 [cs, stat] (Dec.","year":"2018","author":"Tran Dustin","key":"e_1_3_2_1_32_1","unstructured":"Dustin Tran , Michael W. Dusenberry , Mark van der Wilk , and Danijar Hafner . 2018 . Bayesian Layers: A Module for Neural Network Uncertainty. arXiv:1812.03973 [cs, stat] (Dec. 2018). http:\/\/arxiv.org\/abs\/1812.03973 Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, and Danijar Hafner. 2018. Bayesian Layers: A Module for Neural Network Uncertainty. arXiv:1812.03973 [cs, stat] (Dec. 2018). http:\/\/arxiv.org\/abs\/1812.03973"},{"volume-title":"BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. arXiv:2002.06715 [cs, stat] (Feb","year":"2020","author":"Wen Yeming","key":"e_1_3_2_1_33_1","unstructured":"Yeming Wen , Dustin Tran , and Jimmy Ba. 2020. BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. arXiv:2002.06715 [cs, stat] (Feb . 2020 ). http:\/\/arxiv.org\/abs\/2002.06715 arXiv: 2002.06715. Yeming Wen, Dustin Tran, and Jimmy Ba. 2020. BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. arXiv:2002.06715 [cs, stat] (Feb. 2020). http:\/\/arxiv.org\/abs\/2002.06715 arXiv: 2002.06715."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220051"}],"event":{"name":"ACM CHIL '20: ACM Conference on Health, Inference, and Learning","sponsor":["ACM Association for Computing Machinery"],"location":"Toronto Ontario Canada","acronym":"ACM CHIL '20"},"container-title":["Proceedings of the ACM Conference on Health, Inference, and Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3368555.3384457","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T10:08:39Z","timestamp":1673086119000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368555.3384457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":34,"alternative-id":["10.1145\/3368555.3384457","10.1145\/3368555"],"URL":"http:\/\/dx.doi.org\/10.1145\/3368555.3384457","relation":{},"subject":[],"published":{"date-parts":[[2020,4,2]]},"assertion":[{"value":"2020-04-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}