{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T03:01:00Z","timestamp":1765422060185,"version":"3.37.3"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1109\/jbhi.2018.2832599","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T18:53:24Z","timestamp":1525287204000},"page":"72-80","source":"Crossref","is-referenced-by-count":16,"title":["A Hierarchical Bayesian Model for Personalized Survival Predictions"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-7748","authenticated-orcid":false,"given":"Alexis","family":"Bellot","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihaela","family":"van der Schaar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Deep multi-task gaussian processes for survival analysis with competing risks","author":"alaa","year":"0","journal-title":"Proc 30th Conf Neural Inf Process Syst"},{"key":"ref38","first-page":"910","article-title":"Tree-based bayesian mixture model for competing risks","author":"bellot","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1111\/ajt.13030"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/1471-2105-9-14","article-title":"Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models","volume":"9","author":"binder","year":"2008","journal-title":"BMC Bioinformat"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780080803"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v050.i11"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1002\/ejhf.30"},{"journal-title":"ABC of Multimorbidity","year":"2014","author":"mercer","key":"ref36"},{"key":"ref35","first-page":"40","article-title":"What is the bonferroni correction","volume":"6","author":"napierala","year":"2012","journal-title":"AAOS Now"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2012.01.059"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1198\/106186006X133933"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11842","article-title":"Deephit: A deep learning approach to survival analysis with competing risks","author":"lee","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOAS285"},{"key":"ref12","article-title":"Bartmachine: Machine learning with bayesian additive regression trees","author":"kapelner","year":"2013","journal-title":"arXiv 1312 2171"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6893"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-017-0383-8"},{"key":"ref15","article-title":"Machine learning model interpretability for precision medicine","author":"katuwal","year":"2016","journal-title":"arXiv 1610 09045"},{"key":"ref16","article-title":"European union regulations on algorithmic decision-making and a&#x201D; right to explanation","author":"goodman","year":"2016","journal-title":"arXiv 1606 08813"},{"key":"ref17","first-page":"79","article-title":"Hierarchical bayesian survival analysis and projective covariate selection in cardiovascular event risk prediction","volume":"1218","author":"peltola","year":"0","journal-title":"Proc 11th UAI Conf Bayesian Modeling Appl Workshop"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq660"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2174\/1875692111201010022"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1214\/ss\/1009212815","article-title":"Bayesian backfitting (with comments and a rejoinder by the authors","volume":"15","author":"hastie","year":"2000","journal-title":"Statist Sci"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2014.02.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0b013e3181d57473"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.112.100123"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5681"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2187458"},{"key":"ref8","first-page":"244","author":"hosmer","year":"2008","journal-title":"Semi-parametric regression model"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life tables (with discussion)","volume":"34","author":"cox","year":"1972","journal-title":"J Roy Statist Soc Series B"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/biot.201100297"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOAS169"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195393804.003.0001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0011"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"Ann Statist"},{"key":"ref41","article-title":"Bayesian inference of individualized treatment effects using multi-task gaussian processes","author":"alaa","year":"0","journal-title":"Proc 30th Conf Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2014.907095"},{"key":"ref26","first-page":"2956","article-title":"Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery","author":"schulam","year":"0","journal-title":"Proc AAAI"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2836"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/8602373\/08353457.pdf?arnumber=8353457","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T12:46:47Z","timestamp":1720270007000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8353457\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":41,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2018.2832599","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"type":"print","value":"2168-2194"},{"type":"electronic","value":"2168-2208"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}