{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:36:27Z","timestamp":1722990987207},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000102","name":"Health Resources and Services Administration","doi-asserted-by":"publisher","award":["234-2005-370011C"],"id":[{"id":"10.13039\/100000102","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.artmed.2024.102898","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T17:42:15Z","timestamp":1716486135000},"page":"102898","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate"],"prefix":"10.1016","volume":"154","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8645-051X","authenticated-orcid":false,"given":"George H.","family":"Chen","sequence":"first","affiliation":[]},{"given":"Linhong","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6160-4081","authenticated-orcid":false,"given":"Ren","family":"Zuo","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9282-9921","authenticated-orcid":false,"given":"Amanda","family":"Coston","sequence":"additional","affiliation":[]},{"given":"Jeremy C.","family":"Weiss","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.artmed.2024.102898_b1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"10.1016\/j.artmed.2024.102898_b2","doi-asserted-by":"crossref","DOI":"10.18637\/jss.v039.i05","article-title":"Regularization paths for Cox\u2019s proportional hazards model via coordinate descent","author":"Simon","year":"2011","journal-title":"J Stat Softw"},{"issue":"2","key":"10.1016\/j.artmed.2024.102898_b3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/sim.4780030207","article-title":"Regression modelling strategies for improved prognostic prediction","volume":"3","author":"Harrell","year":"1984","journal-title":"Stat Med"},{"year":"2012","series-title":"Survival-supervised latent Dirichlet allocation models for genomic analysis of time-to-event outcomes","author":"Dawson","key":"10.1016\/j.artmed.2024.102898_b4"},{"key":"10.1016\/j.artmed.2024.102898_b5","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.artmed.2024.102898_b6","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. In: International Conference on Learning Representations. 2015."},{"key":"10.1016\/j.artmed.2024.102898_b7","unstructured":"Eisenstein J, Ahmed A, Xing EP. Sparse additive generative models of text. In: International Conference on Machine Learning. 2011, p. 1041\u20138."},{"issue":"1","key":"10.1016\/j.artmed.2024.102898_b8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1093\/biomet\/65.1.167","article-title":"Linear rank tests with right censored data","volume":"65","author":"Prentice","year":"1978","journal-title":"Biometrika"},{"key":"10.1016\/j.artmed.2024.102898_b9","doi-asserted-by":"crossref","unstructured":"Card D, Tan C, Smith NA. Neural Models for Documents with Metadata. In: Proceedings of Association for Computational Linguistics. 2018.","DOI":"10.18653\/v1\/P18-1189"},{"issue":"24","key":"10.1016\/j.artmed.2024.102898_b10","article-title":"DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network","volume":"18","author":"Katzman","year":"2018","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.artmed.2024.102898_b11","doi-asserted-by":"crossref","unstructured":"Lee C, Zame WR, Yoon J, van der Schaar M. DeepHit: A deep learning approach to survival analysis with competing risks. In: AAAI Conference on Artificial Intelligence. 2018.","DOI":"10.1609\/aaai.v32i1.11842"},{"issue":"2","key":"10.1016\/j.artmed.2024.102898_b12","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"10.1016\/j.artmed.2024.102898_b13","doi-asserted-by":"crossref","unstructured":"Schofield A, Magnusson M, Mimno D. Pulling out the stops: Rethinking stopword removal for topic models. In: Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 2017, p. 432\u20136.","DOI":"10.18653\/v1\/E17-2069"},{"key":"10.1016\/j.artmed.2024.102898_b14","doi-asserted-by":"crossref","unstructured":"Porteous I, Newman D, Ihler A, Asuncion A, Smyth P, Welling M. Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2008, p. 569\u201377.","DOI":"10.1145\/1401890.1401960"},{"key":"10.1016\/j.artmed.2024.102898_b15","unstructured":"Srivastava A, Sutton C. Autoencoding variational inference for topic models. In: International Conference on Learning Representations. 2017."},{"key":"10.1016\/j.artmed.2024.102898_b16","unstructured":"Lafferty JD, Blei DM. Correlated Topic Models. In: Adv Neural Inf Process Syst. 2006."},{"key":"10.1016\/j.artmed.2024.102898_b17","unstructured":"McAuliffe JD, Blei DM. Supervised Topic Models. In: Adv Neural Inf Process Syst. 2008."},{"key":"10.1016\/j.artmed.2024.102898_b18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1162\/tacl_a_00325","article-title":"Topic modeling in embedding spaces","volume":"8","author":"Dieng","year":"2020","journal-title":"Trans Assoc Comput Linguist"},{"key":"10.1016\/j.artmed.2024.102898_b19","doi-asserted-by":"crossref","unstructured":"Zhao H, Phung D, Huynh V, Jin Y, Du L, Buntine W. Topic Modelling Meets Deep Neural Networks: A Survey. In: International Joint Conference on Artificial Intelligence Survey Track. 2021, p. 4713\u201320.","DOI":"10.24963\/ijcai.2021\/638"},{"year":"2002","series-title":"The Statistical Analysis of Failure Time Data","author":"Kalbfleisch","key":"10.1016\/j.artmed.2024.102898_b20"},{"year":"2006","series-title":"Survival Analysis: Techniques for Censored and Truncated Data","author":"Klein","key":"10.1016\/j.artmed.2024.102898_b21"},{"key":"10.1016\/j.artmed.2024.102898_b22","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1002\/sim.2427","article-title":"A time-dependent discrimination index for survival data","volume":"24","author":"Antolini","year":"2005","journal-title":"Stat Med"},{"issue":"2","key":"10.1016\/j.artmed.2024.102898_b23","first-page":"216","article-title":"Discussion of the paper by DR Cox (1972)","volume":"34","author":"Breslow","year":"1972","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"10.1016\/j.artmed.2024.102898_b24","unstructured":"Chapfuwa P, Tao C, Li C, Page C, Goldstein B, Duke LC, Henao R. Adversarial time-to-event modeling. In: International Conference on Machine Learning. 2018, p. 735\u201344."},{"issue":"129","key":"10.1016\/j.artmed.2024.102898_b25","first-page":"1","article-title":"Time-to-event prediction with neural networks and Cox regression","volume":"20","author":"Kvamme","year":"2019","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.artmed.2024.102898_b26","unstructured":"Raykar VC, Steck H, Krishnapuram B, Dehing-oberije C, Lambin P. On Ranking in Survival Analysis: Bounds on the Concordance Index. In: Adv Neural Inf Process Syst. 2007."},{"key":"10.1016\/j.artmed.2024.102898_b27","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/sim.4780140108","article-title":"A neural network model for survival data","volume":"14","author":"Faraggi","year":"1995","journal-title":"Stat Med"},{"key":"10.1016\/j.artmed.2024.102898_b28","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1007\/s10985-021-09532-6","article-title":"Continuous and discrete-time survival prediction with neural networks","volume":"27","author":"Kvamme","year":"2021","journal-title":"Lifetime Data Anal"},{"key":"10.1016\/j.artmed.2024.102898_b29","unstructured":"Kingma DP, Welling M. Auto-encoding variational Bayes. In: International Conference on Learning Representations. 2014."},{"key":"10.1016\/j.artmed.2024.102898_b30","unstructured":"Rezende DJ, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning. 2014, p. 1278\u201386."},{"issue":"3","key":"10.1016\/j.artmed.2024.102898_b31","doi-asserted-by":"crossref","first-page":"191","DOI":"10.7326\/0003-4819-122-3-199502010-00007","article-title":"The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults","volume":"122","author":"Knaus","year":"1995","journal-title":"Ann Intern Med"},{"year":"2015","series-title":"Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis","author":"Harrell","key":"10.1016\/j.artmed.2024.102898_b32"},{"issue":"7403","key":"10.1016\/j.artmed.2024.102898_b33","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"year":"2016","series-title":"MIMIC-III Clinical Database (version 1.4)","author":"Johnson","key":"10.1016\/j.artmed.2024.102898_b34"},{"key":"10.1016\/j.artmed.2024.102898_b35","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"issue":"3","key":"10.1016\/j.artmed.2024.102898_b36","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"Ann Appl Stat"},{"key":"10.1016\/j.artmed.2024.102898_b37","unstructured":"Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions. In: Adv Neural Inf Process Syst. 2017."},{"key":"10.1016\/j.artmed.2024.102898_b38","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.artmed.2024.102898_b39","series-title":"Text Mining: Classification, Clustering, and Applications","article-title":"Topic models","author":"Blei","year":"2009"},{"key":"10.1016\/j.artmed.2024.102898_b40","doi-asserted-by":"crossref","unstructured":"Alokaili A, Aletras N, Stevenson M. Re-Ranking Words to Improve Interpretability of Automatically Generated Topics. In: International Conference on Computational Semantics - Long Papers. 2019, p. 43\u201354.","DOI":"10.18653\/v1\/W19-0404"},{"key":"10.1016\/j.artmed.2024.102898_b41","unstructured":"Chen C, Li O, Tao C, Barnett AJ, Su J, Rudin C. This Looks Like That: Deep Learning for Interpretable Image Recognition. In: Adv Neural Inf Process Syst. 2019."},{"key":"10.1016\/j.artmed.2024.102898_b42","doi-asserted-by":"crossref","unstructured":"Ming Y, Xu P, Qu H, Ren L. Interpretable and steerable sequence learning via prototypes. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, p. 903\u201313.","DOI":"10.1145\/3292500.3330908"},{"key":"10.1016\/j.artmed.2024.102898_b43","unstructured":"Bahri D, Jiang H. Locally Adaptive Label Smoothing for Predictive Churn. In: International Conference on Machine Learning. 2021, p. 532\u201342."},{"issue":"3","key":"10.1016\/j.artmed.2024.102898_b44","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0194985","article-title":"Personalized survival predictions via trees of predictors: An application to cardiac transplantation","volume":"13","author":"Yoon","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.artmed.2024.102898_b45","unstructured":"Lipton ZC, Kale DC, Wetzel R. Modeling missing data in clinical time series with RNNs. In: Machine Learning for Healthcare. 2016."},{"key":"10.1016\/j.artmed.2024.102898_b46","first-page":"186","article-title":"The data model concept in statistical mapping","volume":"7","author":"Jenks","year":"1967","journal-title":"Int Yearb Cartogr"},{"key":"10.1016\/j.artmed.2024.102898_b47","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1007\/s10994-021-06117-0","article-title":"Optimal survival trees","volume":"111","author":"Bertsimas","year":"2022","journal-title":"Mach Learn"},{"key":"10.1016\/j.artmed.2024.102898_b48","unstructured":"Johnson N, Parbhoo S, Ross A, Doshi-Velez F. Learning Predictive and Interpretable Timeseries Summaries from ICU Data. In: AMIA Annu Symp Proc. 2021, p. 581\u201390."}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365724001404?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365724001404?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T10:42:07Z","timestamp":1722940927000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365724001404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":48,"alternative-id":["S0933365724001404"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2024.102898","relation":{},"ISSN":["0933-3657"],"issn-type":[{"type":"print","value":"0933-3657"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2024.102898","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102898"}}