{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:03:43Z","timestamp":1760058223900,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FPT Software Company Limited","award":["NSF 2223793 EFRI BRAID"],"award-info":[{"award-number":["NSF 2223793 EFRI BRAID"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation (NSF)","doi-asserted-by":"publisher","award":["NSF 2223793 EFRI BRAID"],"award-info":[{"award-number":["NSF 2223793 EFRI BRAID"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can transpire at any given point, rendering event analysis a continuous concern. Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce SSL-SurvFormer. This entails a continuously monotonic Transformer network, empowered by SSL pre-training, that is designed to address the challenges presented by continuous events and absent features in survival prediction. Our proposed continuously monotonic Transformer model facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. Additionally, our SSL pre-training strategy incorporates data transformation to adeptly manage missing information. The SSL pre-training encompasses two tasks: mask prediction, which identifies positions of absent features, and reconstruction, which endeavors to recover absent elements based on observed ones. Our empirical evaluations conducted across a variety of datasets, including FLCHAIN, METABRIC, and SUPPORT, consistently highlight the superior performance of SSL-SurvFormer in comparison to existing methods. Additionally, SSL-SurvFormer demonstrates effectiveness in handling missing values, a critical aspect often encountered in real-world datasets.<\/jats:p>","DOI":"10.3390\/informatics12010032","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T10:38:48Z","timestamp":1742380728000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis"],"prefix":"10.3390","volume":"12","author":[{"given":"Quang-Hung","family":"Le","sequence":"first","affiliation":[{"name":"FPT Software AI Centre, Hanoi 100000, Vietnam"},{"name":"A<sup>2<\/sup>I<sup>2<\/sup> Applied Artificial Intelligence Institute, Deakin University, Waurn Ponds, VIC 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brijesh","family":"Patel","sequence":"additional","affiliation":[{"name":"School of Medicine, West Virginia University, Morgantown, WV 26506, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donald","family":"Adjeroh","sequence":"additional","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering (LCSEE), Morgantown, WV 26506, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-6646","authenticated-orcid":false,"given":"Gianfranco","family":"Doretto","sequence":"additional","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering (LCSEE), Morgantown, WV 26506, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ngan","family":"Le","sequence":"additional","affiliation":[{"name":"AICV Laboratory, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1111\/acel.12121","article-title":"Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv","volume":"12","author":"Ziehm","year":"2013","journal-title":"Aging Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/TII.2014.2349359","article-title":"Machine Learning for Predictive Maintenance: A Multiple Classifier Approach","volume":"11","author":"Susto","year":"2015","journal-title":"IEEE Tran. 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