{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:44:11Z","timestamp":1776077051629,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["92046010 82161148011 61872369"],"award-info":[{"award-number":["92046010 82161148011 61872369"]}]},{"name":"National Key R&D Program of China","award":["2019YFB2102103"],"award-info":[{"award-number":["2019YFB2102103"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467069","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"3503-3511","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["RAPT: Pre-training of Time-Aware Transformer for Learning Robust Healthcare Representation"],"prefix":"10.1145","author":[{"given":"Houxing","family":"Ren","sequence":"first","affiliation":[{"name":"Beihang University & Peng Cheng Laboratory, Beijing, China"}]},{"given":"Jingyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University & Peng Cheng Laboratory, Beijing, China"}]},{"given":"Wayne Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China"}]},{"given":"Ning","family":"Wu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E. Hinton","author":"Ba Lei Jimmy","year":"2016","unstructured":"Lei Jimmy Ba , Jamie Ryan Kiros, and Geoffrey E. Hinton . 2016 . Layer Normalization. CoRR , Vol. abs\/ 1607 .06450 (2016). Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normalization. CoRR, Vol. abs\/1607.06450 (2016)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Tian Bai Shanshan Zhang Brian L. Egleston and Slobodan Vucetic. 2018. Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time. In KDD. ACM 43--51.  Tian Bai Shanshan Zhang Brian L. Egleston and Slobodan Vucetic. 2018. Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time. In KDD. ACM 43--51.","DOI":"10.1145\/3219819.3219904"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Inci M. Baytas Cao Xiao Xi Zhang Fei Wang Anil K. Jain and Jiayu Zhou. 2017. Patient Subtyping via Time-Aware LS\u2122 Networks. In KDD. ACM 65--74.  Inci M. Baytas Cao Xiao Xi Zhang Fei Wang Anil K. Jain and Jiayu Zhou. 2017. Patient Subtyping via Time-Aware LS\u2122 Networks. In KDD. ACM 65--74.","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI200524531"},{"key":"e_1_3_2_2_5_1","unstructured":"Mathilde Caron Ishan Misra Julien Mairal Priya Goyal Piotr Bojanowski and Armand Joulin. 2020. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. In NeurIPS .  Mathilde Caron Ishan Misra Julien Mairal Priya Goyal Piotr Bojanowski and Armand Joulin. 2020. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. In NeurIPS ."},{"key":"e_1_3_2_2_6_1","volume-title":"Mohammad Taha Bahadori, and Yan Liu","author":"Che Zhengping","year":"2015","unstructured":"Zhengping Che , David C. Kale , Wenzhe Li , Mohammad Taha Bahadori, and Yan Liu . 2015 . Deep Computational Phenotyping. In KDD. ACM , 507--516. Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu. 2015. Deep Computational Phenotyping. In KDD. ACM, 507--516."},{"key":"e_1_3_2_2_7_1","volume-title":"Hinton","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey E . Hinton . 2020 . A Simple Framework for Contrastive Learning of Visual Representations. In ICML (Proceedings of Machine Learning Research , Vol. 119). PMLR, 1597-- 1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607."},{"key":"e_1_3_2_2_8_1","volume-title":"Risk Prediction with Electronic Health Records: A Deep Learning Approach","author":"Cheng Yu","unstructured":"Yu Cheng , Fei Wang , Ping Zhang , and Jianying Hu. 2016. Risk Prediction with Electronic Health Records: A Deep Learning Approach . In SDM. SIAM , 432--440. Yu Cheng, Fei Wang, Ping Zhang, and Jianying Hu. 2016. Risk Prediction with Electronic Health Records: A Deep Learning Approach. In SDM. SIAM, 432--440."},{"key":"e_1_3_2_2_9_1","volume-title":"Le Song, Walter F. Stewart, and Jimeng Sun.","author":"Choi Edward","year":"2017","unstructured":"Edward Choi , Mohammad Taha Bahadori , Le Song, Walter F. Stewart, and Jimeng Sun. 2017 . GRAM : Graph-based Attention Model for Healthcare Representation Learning. In KDD. ACM , 787--795. Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, and Jimeng Sun. 2017. GRAM: Graph-based Attention Model for Healthcare Representation Learning. In KDD. ACM, 787--795."},{"key":"e_1_3_2_2_10_1","volume-title":"Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart. 2016 . RETAIN : An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In NIPS. 3504--3512. Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart. 2016. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In NIPS. 3504--3512."},{"key":"e_1_3_2_2_11_1","volume-title":"CVPR (1)","author":"Chopra Sumit","unstructured":"Sumit Chopra , Raia Hadsell , and Yann LeCun . 2005. Learning a Similarity Metric Discriminatively, with Application to Face Verification . In CVPR (1) . IEEE Computer Society , 539--546. Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005. Learning a Similarity Metric Discriminatively, with Application to Face Verification. In CVPR (1). IEEE Computer Society, 539--546."},{"key":"e_1_3_2_2_12_1","volume-title":"Manning","author":"Clark Kevin","year":"2020","unstructured":"Kevin Clark , Minh-Thang Luong , Quoc V. Le , and Christopher D . Manning . 2020 . ELECTRA : Pre-training Text Encoders as Discriminators Rather Than Generators. In ICLR. OpenReview .net. Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In ICLR. OpenReview.net."},{"key":"e_1_3_2_2_13_1","volume-title":"Le","author":"Dai Andrew M.","year":"2015","unstructured":"Andrew M. Dai and Quoc V . Le . 2015 . Semi-supervised Sequence Learning. In NIPS. 3079--3087. Andrew M. Dai and Quoc V. Le. 2015. Semi-supervised Sequence Learning. In NIPS. 3079--3087."},{"key":"e_1_3_2_2_14_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1) . Association for Computational Linguistics , 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171--4186."},{"key":"e_1_3_2_2_15_1","volume-title":"Martin A. Riedmiller, and Thomas Brox.","author":"Dosovitskiy Alexey","year":"2014","unstructured":"Alexey Dosovitskiy , Jost Tobias Springenberg , Martin A. Riedmiller, and Thomas Brox. 2014 . Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. In NIPS. 766--774. Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, and Thomas Brox. 2014. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. In NIPS. 766--774."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Junyi Gao Cao Xiao Yasha Wang Wen Tang Lucas M. Glass and Jimeng Sun. 2020. StageNet: Stage-Aware Neural Networks for Health Risk Prediction. In WWW. ACM \/ IW3C2 530--540.  Junyi Gao Cao Xiao Yasha Wang Wen Tang Lucas M. Glass and Jimeng Sun. 2020. StageNet: Stage-Aware Neural Networks for Health Risk Prediction. In WWW. ACM \/ IW3C2 530--540.","DOI":"10.1145\/3366423.3380136"},{"key":"e_1_3_2_2_17_1","volume-title":"Girshick","author":"He Kaiming","year":"2020","unstructured":"Kaiming He , Haoqi Fan , Yuxin Wu , Saining Xie , and Ross B . Girshick . 2020 . Momentum Contrast for Unsupervised Visual Representation Learning. In CVPR. IEEE , 9726--9735. Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020. Momentum Contrast for Unsupervised Visual Representation Learning. In CVPR. IEEE, 9726--9735."},{"key":"e_1_3_2_2_18_1","volume-title":"Deep Residual Learning for Image Recognition","author":"He Kaiming","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016. Deep Residual Learning for Image Recognition . In CVPR. IEEE Computer Society , 770--778. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. IEEE Computer Society, 770--778."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_20_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In ICLR (Poster) . Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR (Poster) ."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.14310\/horm.2002.1582"},{"key":"e_1_3_2_2_22_1","volume-title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net.","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan , Mingda Chen , Sebastian Goodman , Kevin Gimpel , Piyush Sharma , and Radu Soricut . 2020 . ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Junyu Luo Muchao Ye Cao Xiao and Fenglong Ma. 2020. HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records. In KDD. ACM 647--656.  Junyu Luo Muchao Ye Cao Xiao and Fenglong Ma. 2020. HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records. In KDD. ACM 647--656.","DOI":"10.1145\/3394486.3403107"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"e_1_3_2_2_25_1","volume-title":"Self-Supervised Learning of pre-training-Invariant Representations","author":"Misra Ishan","unstructured":"Ishan Misra and Laurens van der Maaten . 2020. Self-Supervised Learning of pre-training-Invariant Representations . In CVPR. IEEE , 6706--6716. Ishan Misra and Laurens van der Maaten. 2020. Self-Supervised Learning of pre-training-Invariant Representations. In CVPR. IEEE, 6706--6716."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2633963"},{"key":"e_1_3_2_2_27_1","volume-title":"Sustainable Development Goals","author":"World Health Organization","unstructured":"World Health Organization . 2017. World Health Statistics 2017: Monitoring Health for the SDGs , Sustainable Development Goals . World Health Organization . https:\/\/books.google.com\/books?id=JVXptAEACAAJ World Health Organization. 2017. World Health Statistics 2017: Monitoring Health for the SDGs, Sustainable Development Goals .World Health Organization. https:\/\/books.google.com\/books?id=JVXptAEACAAJ"},{"key":"e_1_3_2_2_28_1","volume-title":"Deep Contextualized Word Representations","author":"Peters Matthew E.","unstructured":"Matthew E. Peters , Mark Neumann , Mohit Iyyer , Matt Gardner , Christopher Clark , Kenton Lee , and Luke Zettlemoyer . 2018. Deep Contextualized Word Representations . In NAACL-HLT. Association for Computational Linguistics , 2227--2237. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. In NAACL-HLT. Association for Computational Linguistics, 2227--2237."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_2_31_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_32_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Jingyuan Wang Ze Wang Jianfeng Li and Junjie Wu. 2018. Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis. In KDD. ACM 2437--2446.  Jingyuan Wang Ze Wang Jianfeng Li and Junjie Wu. 2018. Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis. In KDD. ACM 2437--2446.","DOI":"10.1145\/3219819.3220060"},{"key":"e_1_3_2_2_34_1","unstructured":"Wei Wang Bin Bi Ming Yan Chen Wu Jiangnan Xia Zuyi Bao Liwei Peng and Luo Si. 2020. StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. In ICLR. OpenReview.net.  Wei Wang Bin Bi Ming Yan Chen Wu Jiangnan Xia Zuyi Bao Liwei Peng and Luo Si. 2020. StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. In ICLR. OpenReview.net."},{"key":"e_1_3_2_2_35_1","volume-title":"Andrew Kawai, Cheng Li, Lei Chen, Yu Wang, Jian-Zhong Sheng, Jian-Xia Fan, Yi Shi, et al.","author":"Wu Yan-Ting","year":"2020","unstructured":"Yan-Ting Wu , Chen-Jie Zhang , Ben Willem Mol , Andrew Kawai, Cheng Li, Lei Chen, Yu Wang, Jian-Zhong Sheng, Jian-Xia Fan, Yi Shi, et al. 2020 . Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning. The Journal of Clinical Endocrinology & Metabolism ( 2020). Yan-Ting Wu, Chen-Jie Zhang, Ben Willem Mol, Andrew Kawai, Cheng Li, Lei Chen, Yu Wang, Jian-Zhong Sheng, Jian-Xia Fan, Yi Shi, et al. 2020. Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning. The Journal of Clinical Endocrinology & Metabolism (2020)."},{"key":"e_1_3_2_2_36_1","volume-title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. CoRR","author":"Wu Zhirong","year":"1978","unstructured":"Zhirong Wu , Yuanjun Xiong , Stella X. Yu , and Dahua Lin . 2018. Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. CoRR , Vol. abs\/ 1805 .0 1978 (2018). Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. 2018. Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. CoRR, Vol. abs\/1805.01978 (2018)."},{"key":"e_1_3_2_2_37_1","volume-title":"Unsupervised Embedding Learning via Invariant and Spreading Instance Feature","author":"Ye Mang","unstructured":"Mang Ye , Xu Zhang , Pong C. Yuen , and Shih-Fu Chang . 2019. Unsupervised Embedding Learning via Invariant and Spreading Instance Feature . In CVPR. Computer Vision Foundation \/ IEEE , 6210--6219. Mang Ye, Xu Zhang, Pong C. Yuen, and Shih-Fu Chang. 2019. Unsupervised Embedding Learning via Invariant and Spreading Instance Feature. In CVPR. Computer Vision Foundation \/ IEEE, 6210--6219."},{"key":"e_1_3_2_2_38_1","volume-title":"Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertension in pregnancy","author":"Zhang Jun","year":"2003","unstructured":"Jun Zhang , Susan Meikle , and Ann Trumble . 2003. Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertension in pregnancy , Vol. 22 , 2 ( 2003 ), 203--212. Jun Zhang, Susan Meikle, and Ann Trumble. 2003. Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertension in pregnancy, Vol. 22, 2 (2003), 203--212."},{"key":"e_1_3_2_2_39_1","volume-title":"INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare. In KDD. ACM, 450--460.","author":"Zhang Xianli","year":"2020","unstructured":"Xianli Zhang , Buyue Qian , Shilei Cao , Yang Li , Hang Chen , Yefeng Zheng , and Ian Davidson . 2020 . INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare. In KDD. ACM, 450--460. Xianli Zhang, Buyue Qian, Shilei Cao, Yang Li, Hang Chen, Yefeng Zheng, and Ian Davidson. 2020. INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare. In KDD. ACM, 450--460."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Xi Sheryl Zhang Fengyi Tang Hiroko H. Dodge Jiayu Zhou and Fei Wang. 2019. MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records. In KDD. ACM 2487--2495.  Xi Sheryl Zhang Fengyi Tang Hiroko H. Dodge Jiayu Zhou and Fei Wang. 2019. MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records. In KDD. ACM 2487--2495.","DOI":"10.1145\/3292500.3330779"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467069","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467069","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:11Z","timestamp":1750195511000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":40,"alternative-id":["10.1145\/3447548.3467069","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467069","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}