{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T22:43:42Z","timestamp":1774305822447,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372189"],"award-info":[{"award-number":["62372189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007224","name":"Guangdong Special Funding for Science and Technology Innovation Strategy","doi-asserted-by":"publisher","award":["2024A1111120004"],"award-info":[{"award-number":["2024A1111120004"]}],"id":[{"id":"10.13039\/100007224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Consumer Electron."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tce.2025.3572452","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T13:58:29Z","timestamp":1747922309000},"page":"3340-3349","source":"Crossref","is-referenced-by-count":1,"title":["An Event-Aware Dual Representation Model With Mixture-of-Experts for Serious Adverse Events Prediction in Clinical Trials"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5802-9274","authenticated-orcid":false,"given":"Baoshuo","family":"Kan","sequence":"first","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2331-3619","authenticated-orcid":false,"given":"Teng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Hong Kong, Siu Lek Yuen, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengdong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8514-9263","authenticated-orcid":false,"given":"Rongjiao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-6374","authenticated-orcid":false,"given":"Enliang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-0053","authenticated-orcid":false,"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9792-3949","authenticated-orcid":false,"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2016.6008"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4103\/ijd.IJD_273_17"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.167.16.1752"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18553\/jmcp.2018.24.7.682"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/qjmed\/hcu145"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2174\/1381612822666160509125047"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-1-7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3363896"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3302297"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3274651"},{"key":"ref11","first-page":"1","article-title":"Adverse event prediction using a task-specific generative model","volume-title":"Proc. ICML 3rd Workshop Interpretable Mach. Learn. Healthcare (IMLH)","author":"L\u00f6nnroth"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2007.14.0673"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-023-05243-w"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.crmeth.2022.100358"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.26"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv1075"},{"key":"ref17","first-page":"1","article-title":"Clinical safety data management: Definitions and standards for expedited reporting E2A","volume-title":"Proc. Int. Conf. Harmonisation Tech. Requirements Reg. Pharmaceuticals Human Use","author":"Guideline"},{"key":"ref18","first-page":"2902","article-title":"TransTab: Learning transferable tabular transformers across tables","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","volume":"35","author":"Wang"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100445"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100312"},{"key":"ref21","first-page":"288","article-title":"Predicting phase 3 clinical trial results by modeling phase 2 clinical trial subject level data using deep learning","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Qi"},{"key":"ref22","volume-title":"Machine learning with statistical imputation for predicting drug approvals","author":"Lo","year":"2018"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3584371.3613001"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1994.6.2.181"},{"key":"ref26","first-page":"1","article-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Shazeer"},{"issue":"120","key":"ref27","first-page":"1","article-title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","volume":"23","author":"Fedus","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref28","article-title":"One model, multiple modalities: A sparsely activated approach for text, sound, image, video and code","author":"Dai","year":"2022","journal-title":"arXiv:2205.06126"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3458754"},{"key":"ref30","article-title":"ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction","author":"Chithrananda","year":"2020","journal-title":"arXiv:2010.09885"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref32","first-page":"3780","article-title":"A unified view of multi-label performance measures","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref34","first-page":"1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume-title":"Proc. NIPS Workshop Deep Learn.","author":"Chung"},{"key":"ref35","first-page":"2","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. NAACL-HLT","author":"Devlin"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1907.11692"},{"key":"ref37","article-title":"LongFormer: The long-document transformer","author":"Beltagy","year":"2020","journal-title":"arXiv:2004.05150"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-1909"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref41","first-page":"1","article-title":"DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst. (NIPS)","author":"Sanh"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac225"},{"key":"ref43","article-title":"Fixing weight decay regularization in Adam","author":"Loshchilov","year":"2017","journal-title":"arXiv:1711.05101"}],"container-title":["IEEE Transactions on Consumer Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/30\/11128999\/11010096.pdf?arnumber=11010096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:36:47Z","timestamp":1760117807000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11010096\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":43,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tce.2025.3572452","relation":{},"ISSN":["0098-3063","1558-4127"],"issn-type":[{"value":"0098-3063","type":"print"},{"value":"1558-4127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}