Tensor-Based Temporal Multi-Task Survival Analysis

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dc.contributor.authorWang, Pingen
dc.contributor.authorShi, Tianen
dc.contributor.authorReddy, Chandan K.en
dc.date.accessioned2022-01-19T22:08:41Zen
dc.date.available2022-01-19T22:08:41Zen
dc.date.issued2021-09-01en
dc.date.updated2022-01-19T22:08:40Zen
dc.description.abstractSurvival analysis aims at predicting the time to event of interest along with its probability on longitudinal data. It is commonly used to make predictions for a single specific event of interest at a given time point. However, predicting the occurrence of multiple events of interest simultaneously and dynamically is needed in many real-world applications. An intuitive way to solve this problem is to simply apply the standard survival analysis method independently to each prediction task at each time point. However, it often leads to a sub-optimal solution since the underlying dependencies between these tasks are ignored. This motivates us to analyze these prediction tasks jointly in order to select the common features shared across all the tasks. In this paper, we formulate a temporal (Multiple Time points) Multi-Task learning framework (MTMT) for survival analysis problems using tensor representation. More specifically, given a survival dataset and a sequence of time points, which are considered as the monitored time points for the events of interest, we reformulate the survival analysis problem to jointly handle each task at each time point and optimize them simultaneously. We demonstrate the performance of the proposed MTMT model on important real-world datasets, including employee attrition and medical records. We show the superior performance of the MTMT model compared to several state-of-the-art models using standard metrics. We also provide the list of important features selected by our MTMT model thus demonstrating the interpretability of the proposed model.en
dc.description.versionAccepted versionen
dc.format.extentPages 3311-3322en
dc.format.extent12 page(s)en
dc.identifier.doihttps://doi.org/10.1109/TKDE.2020.2967700en
dc.identifier.eissn1558-2191en
dc.identifier.issn1041-4347en
dc.identifier.issue9en
dc.identifier.orcidReddy, Chandan [0000-0003-2839-3662]en
dc.identifier.urihttp://hdl.handle.net/10919/107798en
dc.identifier.volume33en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000682116800012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Artificial Intelligenceen
dc.subjectComputer Science, Information Systemsen
dc.subjectEngineering, Electrical & Electronicen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectTask analysisen
dc.subjectStandardsen
dc.subjectCorrelationen
dc.subjectAnalytical modelsen
dc.subjectLearning systemsen
dc.subjectTensorsen
dc.subjectPrediction algorithmsen
dc.subjectMulti-task learningen
dc.subjectsurvival analysisen
dc.subjecttemporal modelsen
dc.subjectregularizationen
dc.subjectregression analysisen
dc.subjectREGRESSIONen
dc.subjectALGORITHMen
dc.subjectInformation Systemsen
dc.subject08 Information and Computing Sciencesen
dc.titleTensor-Based Temporal Multi-Task Survival Analysisen
dc.title.serialIEEE Transactions on Knowledge and Data Engineeringen
dc.typeArticle - Refereeden
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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