{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T02:57:18Z","timestamp":1770173838846,"version":"3.49.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61672450"],"award-info":[{"award-number":["61672450"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named\n            <jats:italic>AttenSurv<\/jats:italic>\n            , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential\/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named\n            <jats:italic>GNNAttenSurv<\/jats:italic>\n            , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.\n          <\/jats:p>","DOI":"10.1145\/3466782","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T10:15:37Z","timestamp":1631614537000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Attention-Based Deep Recurrent Model for Survival Prediction"],"prefix":"10.1145","volume":"2","author":[{"given":"Zhaohong","family":"Sun","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Dong","sequence":"additional","affiliation":[{"name":"Chinese PLA General Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlong","family":"Shi","sequence":"additional","affiliation":[{"name":"Chinese PLA General Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunlun","family":"He","sequence":"additional","affiliation":[{"name":"Chinese PLA General Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294993"},{"key":"e_1_2_1_2_1","article-title":"Deep representation learning for individualized treatment effect estimation using electronic health records","author":"Chen Peipei","year":"2019","journal-title":"Journal of Biomedical Informatics 100"},{"key":"e_1_2_1_3_1","first-page":"2","article-title":"Regression models and life-tables","volume":"34","author":"Cox David","year":"1972","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"e_1_2_1_4_1","first-page":"7403","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis Christina","year":"2012","journal-title":"Nature"},{"key":"e_1_2_1_5_1","first-page":"7","article-title":"On clinical event prediction in patient treatment trajectory using longitudinal electronic health records","volume":"24","author":"Duan Huilong","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157658"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/52.3-4.650"},{"key":"e_1_2_1_8_1","first-page":"2","article-title":"Left atrial size and risk of major cardiovascular events during antihypertensive treatment: Losartan intervention for endpoint reduction in hypertension trial","volume":"49","author":"Gerdts Eva","year":"2006","journal-title":"Hypertension"},{"key":"e_1_2_1_9_1","first-page":"2","article-title":"A simple test of the proportional hazards assumption","volume":"74","author":"Gill Richard","year":"1987","journal-title":"Biometrika"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN\u201918)","author":"Giunchiglia Eleonora"},{"key":"e_1_2_1_11_1","volume-title":"N-terminal fragment of probrain natriuretic peptide is associated with diabetes microvascular complications in type 2 diabetes. 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