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Predicting survival time for sepsis patients is a time-critical prediction problem. Considering the useful sequential information for sepsis development, this paper proposes a time-critical topic model (TiCTM) inspired by the latent Dirichlet allocation (LDA) model. The proposed TiCTM approach takes into account the time dependency structure between notes, measurement, and survival time of a sepsis patient. Experimental results on the public MIMIC-III database show that, overall, our method outperforms the conventional LDA and linear regression model in terms of recall, precision, accuracy, and F1-measure. It is also found that our method achieves the best performance by using 5 topics when predicting the probability for 30-day survival time.<\/jats:p>","DOI":"10.1155\/2020\/8884539","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T23:36:04Z","timestamp":1600212964000},"page":"1-13","source":"Crossref","is-referenced-by-count":3,"title":["A Time-Critical Topic Model for Predicting the Survival Time of Sepsis Patients"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0405-1775","authenticated-orcid":true,"given":"Wenping","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Intelligent Information Processing, Taizhou University, Taizhou 318000, China"},{"name":"College of Computer and Information, Hohai University, Nanjing 210098, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoming","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and 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