{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:56Z","timestamp":1750220396354,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Quanta Computer, Inc."}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,8]]},"DOI":"10.1145\/3450439.3451869","type":"proceedings-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T22:25:27Z","timestamp":1616538327000},"page":"95-104","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning to predict with supporting evidence"],"prefix":"10.1145","author":[{"given":"Aniruddh","family":"Raghu","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"John","family":"Guttag","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Katherine","family":"Young","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Eugene","family":"Pomerantsev","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital"}]},{"given":"Adrian V.","family":"Dalca","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital"}]},{"given":"Collin M.","family":"Stultz","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital"}]}],"member":"320","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Contextual explanation networks. arXiv preprint arXiv:1705.10301","author":"Al-Shedivat Maruan","year":"2017","unstructured":"Maruan Al-Shedivat , Avinava Dubey , and Eric P Xing . 2017. Contextual explanation networks. arXiv preprint arXiv:1705.10301 ( 2017 ). Maruan Al-Shedivat, Avinava Dubey, and Eric P Xing. 2017. Contextual explanation networks. arXiv preprint arXiv:1705.10301 (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.284.7.835"},{"key":"e_1_3_2_1_3_1","volume-title":"Comparison of invasive and less-invasive techniques of cardiac output measurement under different haemodynamic conditions in a pig model. European journal of anaesthesiology 23, 1","author":"Bajorat J","year":"2006","unstructured":"J Bajorat , R Hofmockel , DA Vagts , M Janda , B Pohl , C Beck , and G Noeldge-Schomburg . 2006. Comparison of invasive and less-invasive techniques of cardiac output measurement under different haemodynamic conditions in a pig model. European journal of anaesthesiology 23, 1 ( 2006 ), 23--30. J Bajorat, R Hofmockel, DA Vagts, M Janda, B Pohl, C Beck, and G Noeldge-Schomburg. 2006. Comparison of invasive and less-invasive techniques of cardiac output measurement under different haemodynamic conditions in a pig model. European journal of anaesthesiology 23, 1 (2006), 23--30."},{"key":"e_1_3_2_1_4_1","unstructured":"Emelia J Benjamin Paul Muntner Alvaro Alonso Marcio S Bittencourt Clifton W Callaway April P Carson Alanna M Chamberlain Alexander R Chang Susan Cheng Sandeep R Das etal 2019. Heart disease and stroke Statistics-2019 update a report from the American Heart Association. Circulation (2019).  Emelia J Benjamin Paul Muntner Alvaro Alonso Marcio S Bittencourt Clifton W Callaway April P Carson Alanna M Chamberlain Alexander R Chang Susan Cheng Sandeep R Das et al. 2019. Heart disease and stroke Statistics-2019 update a report from the American Heart Association. Circulation (2019)."},{"key":"e_1_3_2_1_5_1","unstructured":"JM Bernardo MJ Bayarri JO Berger AP Dawid D Heckerman AFM Smith M West etal 2003. The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures. (2003).  JM Bernardo MJ Bayarri JO Berger AP Dawid D Heckerman AFM Smith M West et al. 2003. The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures. (2003)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300234"},{"key":"e_1_3_2_1_7_1","unstructured":"Marianne Catanho Mridu Sinha and Varsha Vijayan. 2012. Model of aortic blood flow using the windkessel effect. (2012).  Marianne Catanho Mridu Sinha and Varsha Vijayan. 2012. Model of aortic blood flow using the windkessel effect. (2012)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_40"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376638"},{"key":"e_1_3_2_1_10_1","unstructured":"Amirata Ghorbani James Wexler James Y Zou and Been Kim. 2019. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems. 9273--9282.  Amirata Ghorbani James Wexler James Y Zou and Been Kim. 2019. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems. 9273--9282."},{"key":"e_1_3_2_1_11_1","volume-title":"Alvaro Avezum, Shaun G Goodman, Marcus D Flather, et al.","author":"Granger Christopher B","year":"2003","unstructured":"Christopher B Granger , Robert J Goldberg , Omar Dabbous , Karen S Pieper , Kim A Eagle , Christopher P Cannon , Frans Van de Werf , Alvaro Avezum, Shaun G Goodman, Marcus D Flather, et al. 2003 . Predictors of hospital mortality in the global registry of acute coronary events. Archives of internal medicine 163, 19 (2003), 2345--2353. Christopher B Granger, Robert J Goldberg, Omar Dabbous, Karen S Pieper, Kim A Eagle, Christopher P Cannon, Frans Van de Werf, Alvaro Avezum, Shaun G Goodman, Marcus D Flather, et al. 2003. Predictors of hospital mortality in the global registry of acute coronary events. Archives of internal medicine 163, 19 (2003), 2345--2353."},{"key":"e_1_3_2_1_12_1","volume-title":"Anders Perner, et al.","author":"Hiemstra Bart","year":"2019","unstructured":"Bart Hiemstra , Geert Koster , Renske Wiersema , Yoran M Hummel , Pim van der Harst , Harold Snieder , Ruben J Eck , Thomas Kaufmann , Thomas WL Scheeren , Anders Perner, et al. 2019 . The diagnostic accuracy of clinical examination for estimating cardiac index in critically ill patients: the Simple Intensive Care Studies-I. Intensive care medicine 45, 2 (2019), 190--200. Bart Hiemstra, Geert Koster, Renske Wiersema, Yoran M Hummel, Pim van der Harst, Harold Snieder, Ruben J Eck, Thomas Kaufmann, Thomas WL Scheeren, Anders Perner, et al. 2019. The diagnostic accuracy of clinical examination for estimating cardiac index in critically ill patients: the Simple Intensive Care Studies-I. Intensive care medicine 45, 2 (2019), 190--200."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314273"},{"key":"e_1_3_2_1_14_1","volume-title":"DiffTaichi: Differentiable Programming for Physical Simulation. arXiv preprint arXiv:1910.00935","author":"Hu Yuanming","year":"2019","unstructured":"Yuanming Hu , Luke Anderson , Tzu-Mao Li , Qi Sun , Nathan Carr , Jonathan Ragan-Kelley , and Fr\u00e9do Durand . 2019. DiffTaichi: Differentiable Programming for Physical Simulation. arXiv preprint arXiv:1910.00935 ( 2019 ). Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Fr\u00e9do Durand. 2019. DiffTaichi: Differentiable Programming for Physical Simulation. arXiv preprint arXiv:1910.00935 (2019)."},{"volume-title":"The heart, arteries and veins","author":"Hurst J","key":"e_1_3_2_1_15_1","unstructured":"J Hurst , C Rackley , E Sonnenblick , and N Wenger . 1990. The heart, arteries and veins . Vol. 1 . McGraw-Hill . J Hurst, C Rackley, E Sonnenblick, and N Wenger. 1990. The heart, arteries and veins. Vol. 1. McGraw-Hill."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. 2668--2677","author":"Kim Been","year":"2018","unstructured":"Been Kim , Martin Wattenberg , Justin Gilmer , Carrie Cai , James Wexler , Fernanda Viegas , 2018 . Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) . In International Conference on Machine Learning. 2668--2677 . Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al. 2018. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In International Conference on Machine Learning. 2668--2677."},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","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)."},{"key":"e_1_3_2_1_18_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_19_1","unstructured":"Mary E Klingensmith etal 2008. The Washington manual of surgery. Lippincott Williams & Wilkins.  Mary E Klingensmith et al. 2008. The Washington manual of surgery. Lippincott Williams & Wilkins."},{"key":"e_1_3_2_1_20_1","volume-title":"Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang.","author":"Koh Pang Wei","year":"2020","unstructured":"Pang Wei Koh , Thao Nguyen , Yew Siang Tang , Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020 . Concept Bottleneck Models . arXiv preprint arXiv:2007.04612 (2020). Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. Concept Bottleneck Models. arXiv preprint arXiv:2007.04612 (2020)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLHCC.2013.6645235"},{"key":"e_1_3_2_1_22_1","volume-title":"Rationalizing neural predictions. arXiv preprint arXiv:1606.04155","author":"Lei Tao","year":"2016","unstructured":"Tao Lei , Regina Barzilay , and Tommi Jaakkola . 2016. Rationalizing neural predictions. arXiv preprint arXiv:1606.04155 ( 2016 ). Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing neural predictions. arXiv preprint arXiv:1606.04155 (2016)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-150-9-200905050-00006"},{"key":"e_1_3_2_1_24_1","volume-title":"Thirty-Second AAAI Conference on Artificial Intelligence.","author":"Li Oscar","year":"2018","unstructured":"Oscar Li , Hao Liu , Chaofan Chen , and Cynthia Rudin . 2018 . Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions . In Thirty-Second AAAI Conference on Artificial Intelligence. Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2018. Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In Thirty-Second AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295230"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.05.049"},{"key":"e_1_3_2_1_27_1","unstructured":"David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems. 7775--7784.  David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems. 7775--7784."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.286.11.1356"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-50933-3"},{"key":"e_1_3_2_1_31_1","volume-title":"Identifying unreliable predictions in clinical risk models. NPJ digital medicine 3, 1","author":"Myers Paul D","year":"2020","unstructured":"Paul D Myers , Kenney Ng , Kristen Severson , Uri Kartoun , Wangzhi Dai , Wei Huang , Frederick A Anderson , and Collin M Stultz . 2020. Identifying unreliable predictions in clinical risk models. NPJ digital medicine 3, 1 ( 2020 ), 1--8. Paul D Myers, Kenney Ng, Kristen Severson, Uri Kartoun, Wangzhi Dai, Wei Huang, Frederick A Anderson, and Collin M Stultz. 2020. Identifying unreliable predictions in clinical risk models. NPJ digital medicine 3, 1 (2020), 1--8."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/299068.299082"},{"key":"e_1_3_2_1_33_1","volume-title":"Regularizing Black-box Models for Improved Interpretability. arXiv preprint arXiv:1902.06787","author":"Plumb Gregory","year":"2019","unstructured":"Gregory Plumb , Maruan Al-Shedivat , Eric Xing , and Ameet Talwalkar . 2019. Regularizing Black-box Models for Improved Interpretability. arXiv preprint arXiv:1902.06787 ( 2019 ). Gregory Plumb, Maruan Al-Shedivat, Eric Xing, and Ameet Talwalkar. 2019. Regularizing Black-box Models for Improved Interpretability. arXiv preprint arXiv:1902.06787 (2019)."},{"key":"e_1_3_2_1_34_1","volume-title":"Linyuan Jing, Joshua Stough, Dustin N Hartzel, Joseph B Leader, H Lester Kirchner, Martin C Stumpe, Ashraf Hafez, Arun Nemani, et al.","author":"Raghunath Sushravya","year":"2020","unstructured":"Sushravya Raghunath , Alvaro E Ulloa Cerna , Linyuan Jing, Joshua Stough, Dustin N Hartzel, Joseph B Leader, H Lester Kirchner, Martin C Stumpe, Ashraf Hafez, Arun Nemani, et al. 2020 . Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature Medicine (2020), 1--6. Sushravya Raghunath, Alvaro E Ulloa Cerna, Linyuan Jing, Joshua Stough, Dustin N Hartzel, Joseph B Leader, H Lester Kirchner, Martin C Stumpe, Ashraf Hafez, Arun Nemani, et al. 2020. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature Medicine (2020), 1--6."},{"volume-title":"Summer School on Machine Learning","author":"Rasmussen Carl Edward","key":"e_1_3_2_1_35_1","unstructured":"Carl Edward Rasmussen . 2003. Gaussian processes in machine learning . In Summer School on Machine Learning . Springer , 63--71. Carl Edward Rasmussen. 2003. Gaussian processes in machine learning. In Summer School on Machine Learning. Springer, 63--71."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/3044805.3045035"},{"key":"e_1_3_2_1_37_1","volume-title":"Predicting long-term mortality in older patients after non-ST-segment elevation myocardial infarction: the CRUSADE long-term mortality model and risk score. American heart journal 162, 5","author":"Roe Matthew T","year":"2011","unstructured":"Matthew T Roe , Anita Y Chen , Laine Thomas , Tracy Y Wang , Karen P Alexander , Bradley G Hammill , W Brian Gibler , E Magnus Ohman , and Eric D Peterson . 2011. Predicting long-term mortality in older patients after non-ST-segment elevation myocardial infarction: the CRUSADE long-term mortality model and risk score. American heart journal 162, 5 ( 2011 ), 875--883. Matthew T Roe, Anita Y Chen, Laine Thomas, Tracy Y Wang, Karen P Alexander, Bradley G Hammill, W Brian Gibler, E Magnus Ohman, and Eric D Peterson. 2011. Predicting long-term mortality in older patients after non-ST-segment elevation myocardial infarction: the CRUSADE long-term mortality model and risk score. American heart journal 162, 5 (2011), 875--883."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2828(90)91459-K"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1183\/09059180.00002210"},{"key":"e_1_3_2_1_40_1","volume-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan , Andrea Vedaldi , and Andrew Zisserman . 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 ( 2013 ). Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.0287"},{"key":"e_1_3_2_1_42_1","volume-title":"The Advent of Clinically Useful Deep Learning. JACC. Clinical electrophysiology 5, 5","author":"Stultz Collin M","year":"2019","unstructured":"Collin M Stultz . 2019. The Advent of Clinically Useful Deep Learning. JACC. Clinical electrophysiology 5, 5 ( 2019 ), 587. Collin M Stultz. 2019. The Advent of Clinically Useful Deep Learning. JACC. Clinical electrophysiology 5, 5 (2019), 587."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306024"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1513\/AnnalsATS.201509-599FR","article-title":"The critical role of pulmonary arterial compliance in pulmonary hypertension","volume":"13","author":"Thenappan Thenappan","year":"2016","unstructured":"Thenappan Thenappan , Kurt W Prins , Marc R Pritzker , John Scandurra , Karl Volmers , and E Kenneth Weir . 2016 . The critical role of pulmonary arterial compliance in pulmonary hypertension . Annals of the American Thoracic Society 13 , 2 (2016), 276 -- 284 . Thenappan Thenappan, Kurt W Prins, Marc R Pritzker, John Scandurra, Karl Volmers, and E Kenneth Weir. 2016. The critical role of pulmonary arterial compliance in pulmonary hypertension. Annals of the American Thoracic Society 13, 2 (2016), 276--284.","journal-title":"Annals of the American Thoracic Society"},{"key":"e_1_3_2_1_45_1","volume-title":"What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In Machine Learning for Healthcare Conference. 359--380","author":"Tonekaboni Sana","year":"2019","unstructured":"Sana Tonekaboni , Shalmali Joshi , Melissa D McCradden , and Anna Goldenberg . 2019 . What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In Machine Learning for Healthcare Conference. 359--380 . Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden, and Anna Goldenberg. 2019. What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In Machine Learning for Healthcare Conference. 359--380."},{"key":"e_1_3_2_1_46_1","volume-title":"High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1","author":"Topol Eric J","year":"2019","unstructured":"Eric J Topol . 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1 ( 2019 ), 44--56. Eric J Topol. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1 (2019), 44--56."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1208943"},{"key":"e_1_3_2_1_48_1","volume-title":"The arterial windkessel. Medical & biological engineering & computing 47, 2","author":"Westerhof Nico","year":"2009","unstructured":"Nico Westerhof , Jan-Willem Lankhaar , and Berend E Westerhof . 2009. The arterial windkessel. Medical & biological engineering & computing 47, 2 ( 2009 ), 131--141. Nico Westerhof, Jan-Willem Lankhaar, and Berend E Westerhof. 2009. The arterial windkessel. Medical & biological engineering & computing 47, 2 (2009), 131--141."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2013.05.020"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"}],"event":{"name":"ACM CHIL '21: ACM Conference on Health, Inference, and Learning","sponsor":["ACM Association for Computing Machinery"],"location":"Virtual Event USA","acronym":"ACM CHIL '21"},"container-title":["Proceedings of the Conference on Health, Inference, and Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450439.3451869","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450439.3451869","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:45Z","timestamp":1750191525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450439.3451869"}},"subtitle":["applications to clinical risk prediction"],"short-title":[],"issued":{"date-parts":[[2021,4,8]]},"references-count":50,"alternative-id":["10.1145\/3450439.3451869","10.1145\/3450439"],"URL":"https:\/\/doi.org\/10.1145\/3450439.3451869","relation":{},"subject":[],"published":{"date-parts":[[2021,4,8]]},"assertion":[{"value":"2021-04-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}