{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T09:40:31Z","timestamp":1783676431385,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["III-1526012"],"award-info":[{"award-number":["III-1526012"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000093","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R21HS024581"],"award-info":[{"award-number":["1R21HS024581"]}],"id":[{"id":"10.13039\/100000093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403067","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:17:27Z","timestamp":1597965447000},"page":"249-256","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Hierarchical Attention Propagation for Healthcare Representation Learning"],"prefix":"10.1145","author":[{"given":"Muhan","family":"Zhang","sequence":"first","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher R.","family":"King","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Avidan","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"The unified medical language system (UMLS): integrating biomedical terminology. Nucleic acids research","author":"Bodenreider Olivier","year":"2004","unstructured":"Olivier Bodenreider . 2004. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic acids research , Vol. 32 , suppl_1 ( 2004 ), D267--D270. Olivier Bodenreider. 2004. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic acids research, Vol. 32, suppl_1 (2004), D267--D270."},{"key":"e_1_3_2_2_2_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 ( 2013 ). Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)."},{"key":"e_1_3_2_2_3_1","unstructured":"Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in Neural Information Processing Systems. 4869--4880.  Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in Neural Information Processing Systems. 4869--4880."},{"key":"e_1_3_2_2_4_1","volume-title":"Machine Learning for Healthcare Conference. 301--318","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Andy Schuetz , Walter F Stewart , and Jimeng Sun . 2016 a. Doctor ai: Predicting clinical events via recurrent neural networks . In Machine Learning for Healthcare Conference. 301--318 . Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016a. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference. 301--318."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098126"},{"key":"e_1_3_2_2_6_1","volume-title":"Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 b. Retain : An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems . 3504--3512. Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016b. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems. 3504--3512."},{"key":"e_1_3_2_2_7_1","volume-title":"Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In Advances in Neural Information Processing Systems. 4547--4557.","author":"Choi Edward","year":"2018","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_2_8_1","unstructured":"Jan K Chorowski Dzmitry Bahdanau Dmitriy Serdyuk Kyunghyun Cho and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in neural information processing systems. 577--585.  Jan K Chorowski Dzmitry Bahdanau Dmitriy Serdyuk Kyunghyun Cho and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in neural information processing systems. 577--585."},{"key":"e_1_3_2_2_9_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3837--3845.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3837--3845."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_11_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034."},{"key":"e_1_3_2_2_12_1","volume-title":"Multilayer feedforward networks are universal approximators. Neural networks","author":"Hornik Kurt","year":"1989","unstructured":"Kurt Hornik , Maxwell Stinchcombe , and Halbert White . 1989. Multilayer feedforward networks are universal approximators. Neural networks , Vol. 2 , 5 ( 1989 ), 359--366. Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks, Vol. 2, 5 (1989), 359--366."},{"key":"e_1_3_2_2_13_1","volume-title":"Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology","author":"Jiang Fei","year":"2017","unstructured":"Fei Jiang , Yong Jiang , Hui Zhi , Yi Dong , Hao Li , Sufeng Ma , Yilong Wang , Qiang Dong , Haipeng Shen , and Yongjun Wang . 2017. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology , Vol. 2 , 4 ( 2017 ), 230--243. Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. 2017. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, Vol. 2, 4 (2017), 230--243."},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning. 2323--2332","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin , Regina Barzilay , and Tommi Jaakkola . 2018 . Junction Tree Variational Autoencoder for Molecular Graph Generation . In Proceedings of the 35th International Conference on Machine Learning. 2323--2332 . Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction Tree Variational Autoencoder for Molecular Graph Generation. In Proceedings of the 35th International Conference on Machine Learning. 2323--2332."},{"key":"e_1_3_2_2_15_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 , H Lehman Li-wei, 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), 160035. Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, 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), 160035."},{"key":"e_1_3_2_2_16_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_17_1","first-page":"65","article-title":"Data mining applications in healthcare","volume":"19","author":"Koh Hian Chye","year":"2011","unstructured":"Hian Chye Koh , Gerald Tan , 2011 . Data mining applications in healthcare . Journal of healthcare information management , Vol. 19 , 2 (2011), 65 . Hian Chye Koh, Gerald Tan, et al. 2011. Data mining applications in healthcare. Journal of healthcare information management, Vol. 19, 2 (2011), 65.","journal-title":"Journal of healthcare information management"},{"key":"e_1_3_2_2_18_1","volume-title":"Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493","author":"Li Yujia","year":"2015","unstructured":"Yujia Li , Daniel Tarlow , Marc Brockschmidt , and Richard Zemel . 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 ( 2015 ). Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)."},{"key":"e_1_3_2_2_19_1","unstructured":"Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In Advances in Neural Information Processing Systems. 8228--8239.  Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In Advances in Neural Information Processing Systems. 8228--8239."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271701"},{"key":"e_1_3_2_2_22_1","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In Advances in neural information processing systems. 6338--6347.  Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In Advances in neural information processing systems. 6338--6347."},{"key":"e_1_3_2_2_23_1","volume-title":"Learning continuous hierarchies in the lorentz model of hyperbolic geometry. arXiv preprint arXiv:1806.03417","author":"Nickel Maximilian","year":"2018","unstructured":"Maximilian Nickel and Douwe Kiela . 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. arXiv preprint arXiv:1806.03417 ( 2018 ). Maximilian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. arXiv preprint arXiv:1806.03417 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"International statistical classification of diseases and related health problems","author":"World Health Organization","unstructured":"World Health Organization . 2004. International statistical classification of diseases and related health problems . Vol. 1 . World Health Organization . World Health Organization. 2004. International statistical classification of diseases and related health problems. Vol. 1. World Health Organization."},{"key":"e_1_3_2_2_25_1","unstructured":"Judea Pearl. 1982. Reverend Bayes on inference engines: A distributed hierarchical approach.  Judea Pearl. 1982. Reverend Bayes on inference engines: A distributed hierarchical approach."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the AMIA Symposium. American Medical Informatics Association, 662","author":"Stearns Michael Q","year":"2001","unstructured":"Michael Q Stearns , Colin Price , Kent A Spackman , and Amy Y Wang . 2001 . SNOMED clinical terms: overview of the development process and project status .. In Proceedings of the AMIA Symposium. American Medical Informatics Association, 662 . Michael Q Stearns, Colin Price, Kent A Spackman, and Amy Y Wang. 2001. SNOMED clinical terms: overview of the development process and project status.. In Proceedings of the AMIA Symposium. American Medical Informatics Association, 662."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_31_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_2_32_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_33_1","volume-title":"International conference on machine learning. 2048--2057","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu , Jimmy Ba , Ryan Kiros , Kyunghyun Cho , Aaron Courville , Ruslan Salakhudinov , Rich Zemel , and Yoshua Bengio . 2015 . Show, attend and tell: Neural image caption generation with visual attention . In International conference on machine learning. 2048--2057 . Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning. 2048--2057."},{"key":"e_1_3_2_2_34_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems. 5165--5175.  Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems. 5165--5175."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Muhan Zhang Zhicheng Cui Marion Neumann and Yixin Chen. 2018. An End-to-End Deep Learning Architecture for Graph Classification. In AAAI. 4438--4445.  Muhan Zhang Zhicheng Cui Marion Neumann and Yixin Chen. 2018. An End-to-End Deep Learning Architecture for Graph Classification. In AAAI. 4438--4445.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"e_1_3_2_2_36_1","unstructured":"Muhan Zhang Shali Jiang Zhicheng Cui Roman Garnett and Yixin Chen. 2019. D-VAE: A variational autoencoder for directed acyclic graphs. In Advances in Neural Information Processing Systems. 1586--1598.  Muhan Zhang Shali Jiang Zhicheng Cui Roman Garnett and Yixin Chen. 2019. D-VAE: A variational autoencoder for directed acyclic graphs. In Advances in Neural Information Processing Systems. 1586--1598."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403067","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403067","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:38Z","timestamp":1750200098000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":36,"alternative-id":["10.1145\/3394486.3403067","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403067","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}