{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:29:47Z","timestamp":1771954187306,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":59,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2443639"],"award-info":[{"award-number":["2443639"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH (National Institutes of Health)","doi-asserted-by":"publisher","award":["U54-GM104941"],"award-info":[{"award-number":["U54-GM104941"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,12]]},"DOI":"10.1145\/3765612.3767245","type":"proceedings-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:45:59Z","timestamp":1765388759000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4930-0365","authenticated-orcid":false,"given":"Md Mozaharul","family":"Mottalib","sequence":"first","affiliation":[{"name":"University of Delaware, Newark, DE, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4616-6869","authenticated-orcid":false,"given":"Thao-Ly T","family":"Phan","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-3063","authenticated-orcid":false,"given":"Rahmatollah","family":"Beheshti","sequence":"additional","affiliation":[{"name":"University of Delaware, Newark, DE, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467166"},{"key":"e_1_3_2_2_2_1","volume-title":"Ameya Sunil Mahabaleshwarkar, et al.","author":"Blakeman Aaron","year":"2025","unstructured":"Aaron Blakeman, Aarti Basant, Abhinav Khattar, Adithya Renduchintala, Akhiad Bercovich, Aleksander Ficek, Alexis Bjorlin, Ali Taghibakhshi, Amala Sanjay Deshmukh, Ameya Sunil Mahabaleshwarkar, et al. 2025. Nemotron-h: A family of accurate and efficient hybrid mamba-transformer models. arXiv preprint arXiv:2504.03624 (2025)."},{"key":"e_1_3_2_2_3_1","volume-title":"Transmamba: Fast universal architecture adaption from transformers to mamba. arXiv preprint arXiv:2502.15130","author":"Chen Xiuwei","year":"2025","unstructured":"Xiuwei Chen, Sihao Lin, Xiao Dong, Zisheng Chen, Meng Cao, Jianhua Han, Hang Xu, and Xiaodan Liang. 2025. Transmamba: Fast universal architecture adaption from transformers to mamba. arXiv preprint arXiv:2502.15130 (2025)."},{"key":"e_1_3_2_2_4_1","volume-title":"Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_2_5_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","first-page":"e0317315","DOI":"10.1371\/journal.pone.0317315","article-title":"Association of blood urea nitrogen with 28-day mortality in critically ill patients: A multi-center retrospective study based on the eICU collaborative research database","volume":"20","author":"Deng Ting","year":"2025","unstructured":"Ting Deng, Die Wu, Shan-shan Liu, Xing-lin Chen, Zhen-wei Zhao, and Lanlang Zhang. 2025. Association of blood urea nitrogen with 28-day mortality in critically ill patients: A multi-center retrospective study based on the eICU collaborative research database. Plos one 20, 1 (2025), e0317315.","journal-title":"Plos one"},{"key":"e_1_3_2_2_7_1","volume-title":"EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records. arXiv preprint arXiv:2405.14567","author":"Fallahpour Adibvafa","year":"2024","unstructured":"Adibvafa Fallahpour, Mahshid Alinoori, Arash Afkanpour, and Amrit Krishnan. 2024. EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records. arXiv preprint arXiv:2405.14567 (2024)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11226709"},{"key":"e_1_3_2_2_9_1","volume-title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In First Conference on Language Modeling.","author":"Gu Albert","year":"2024","unstructured":"Albert Gu and Tri Dao. 2024. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In First Conference on Language Modeling."},{"key":"e_1_3_2_2_10_1","volume-title":"Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations.","author":"Gu Albert","year":"2022","unstructured":"Albert Gu, Karan Goel, and Christopher Re. 2022. Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3506719"},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Machine Learning. PMLR, 4353\u20134363","author":"Horn Max","year":"2020","unstructured":"Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt. 2020. Set functions for time series. In International Conference on Machine Learning. PMLR, 4353\u20134363."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_25"},{"key":"e_1_3_2_2_14_1","volume-title":"Leo Anthony Celi, and Roger Mark","author":"Johnson Alistair","year":"2020","unstructured":"Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark. 2020. Mimic-iv. PhysioNet. Available online at: https:\/\/physionet.org\/content\/mimiciv\/1.0\/(accessed August 23, 2021) (2020), 49\u201355."},{"key":"e_1_3_2_2_15_1","volume-title":"Machine Learning for Healthcare Conference. PMLR, 403\u2013422","author":"Labach Alex","year":"2023","unstructured":"Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, and Rahul G Krishnan. 2023. DuETT: dual event time transformer for electronic health records. In Machine Learning for Healthcare Conference. PMLR, 403\u2013422."},{"key":"e_1_3_2_2_16_1","volume-title":"Hi-BEHRT: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records","author":"Li Yikuan","year":"2022","unstructured":"Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, and Kazem Rahimi. 2022. Hi-BEHRT: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records. IEEE journal of biomedical and health informatics 27, 2 (2022), 1106\u20131117."},{"key":"e_1_3_2_2_17_1","volume-title":"Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi.","author":"Li Yikuan","year":"2020","unstructured":"Yikuan Li, Shishir Rao, Jos\u00e9 Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. 2020. BEHRT: transformer for electronic health records. Scientific reports 10, 1 (2020), 7155."},{"key":"e_1_3_2_2_18_1","volume-title":"Transmamba: Flexibly switching between transformer and mamba. arXiv preprint arXiv:2503.24067","author":"Li Yixing","year":"2025","unstructured":"Yixing Li, Ruobing Xie, Zhen Yang, Xingwu Sun, Shuaipeng Li, Weidong Han, Zhanhui Kang, Yu Cheng, Chengzhong Xu, Di Wang, et al. 2025. Transmamba: Flexibly switching between transformer and mamba. arXiv preprint arXiv:2503.24067 (2025)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1200\/JOP.18.00765"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.13063\/2327-9214.1110"},{"key":"e_1_3_2_2_21_1","volume-title":"Discrete event, continuous time rnns. arXiv preprint arXiv:1710.04110","author":"Mozer Michael C","year":"2017","unstructured":"Michael C Mozer, Denis Kazakov, and Robert V Lindsey. 2017. Discrete event, continuous time rnns. arXiv preprint arXiv:1710.04110 (2017)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104438"},{"key":"e_1_3_2_2_23_1","volume-title":"Ana Francisca Torres Sarmento, and Michael Gregorio Ortega Sierra.","author":"Buend\u00eda Palacios Diana Cristina","year":"2023","unstructured":"Diana Cristina Buend\u00eda Palacios, Jo\u00e3o Andr\u00e9 Freitas Silva, Ana Francisca Torres Sarmento, and Michael Gregorio Ortega Sierra. 2023. Mean arterial pressure and outcomes in critically ill patients: is there a difference between high and low target? Revista da Associa\u00e7\u00e3o M\u00e9dica Brasileira 69, 6 (2023), e20230162."},{"key":"e_1_3_2_2_24_1","unstructured":"Chao Pang Xinzhuo Jiang Krishna S Kalluri Matthew Spotnitz RuiJun Chen Adler Perotte and Karthik Natarajan. 2021. CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks. In Machine Learning for Health. PMLR 239\u2013260."},{"key":"e_1_3_2_2_25_1","volume-title":"SiMBA-TS: Simplified Channel Mixing and Mamba for Long-term Time Series Forecasting. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1\u20135.","author":"Patro Badri Narayana","year":"2025","unstructured":"Badri Narayana Patro and Vijay Srinivas Agneeswaran. 2025. SiMBA-TS: Simplified Channel Mixing and Mamba for Long-term Time Series Forecasting. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1\u20135."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CHASE60773.2024.00022"},{"key":"e_1_3_2_2_27_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Poulain Raphael","year":"2024","unstructured":"Raphael Poulain and Rahmatollah Beheshti. 2024. Graph transformers on EHRs: Better representation improves downstream performance. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Alvin Rajkomar Eyal Oren Kai Chen Andrew M Dai Nissan Hajaj Michaela Hardt Peter J Liu Xiaobing Liu Jake Marcus Mimi Sun et al. 2018. Scalable and accurate deep learning with electronic health records. NPJ digital medicine 1 1 (2018) 1\u201310.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"e_1_3_2_2_29_1","volume-title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine 4, 1","author":"Rasmy Laila","year":"2021","unstructured":"Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. 2021. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine 4, 1 (2021), 86."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467069"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-39539-0_7"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.14740\/jocmr4702"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.866260"},{"key":"e_1_3_2_2_34_1","volume-title":"DeepSOFA: a continuous acuity score for critically ill patients using clinically interpretable deep learning. Scientific reports 9, 1","author":"Shickel Benjamin","year":"2019","unstructured":"Benjamin Shickel, Tyler J Loftus, Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Azra Bihorac, and Parisa Rashidi. 2019. DeepSOFA: a continuous acuity score for critically ill patients using clinically interpretable deep learning. Scientific reports 9, 1 (2019), 1879."},{"key":"e_1_3_2_2_35_1","volume-title":"Predicting in-hospital mortality of icu patients: The physionet\/computing in cardiology challenge","author":"Silva Ikaro","year":"2012","unstructured":"Ikaro Silva, George Moody, Daniel J Scott, Leo A Celi, and Roger G Mark. 2012. Predicting in-hospital mortality of icu patients: The physionet\/computing in cardiology challenge 2012. In 2012 computing in cardiology. IEEE, 245\u2013248."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20810"},{"key":"e_1_3_2_2_37_1","volume-title":"Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series. ACM Transactions on Knowledge Discovery from Data (TKDD) 16, 6","author":"Tipirneni Sindhu","year":"2022","unstructured":"Sindhu Tipirneni and Chandan K Reddy. 2022. Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series. ACM Transactions on Knowledge Discovery from Data (TKDD) 16, 6 (2022), 1\u201317."},{"key":"e_1_3_2_2_38_1","volume-title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In International Conference on Learning Representations.","author":"Tonekaboni Sana","year":"2021","unstructured":"Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2021. Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-87574-8"}],"event":{"name":"BCB '25: 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","location":"Element Philadelphia Downtown Philadelphia PA USA","acronym":"BCB '25","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765612.3767245","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765612.3767245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:47:05Z","timestamp":1765388825000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3765612.3767245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,12]]},"references-count":39,"alternative-id":["10.1145\/3765612.3767245","10.1145\/3765612"],"URL":"https:\/\/doi.org\/10.1145\/3765612.3767245","relation":{},"subject":[],"published":{"date-parts":[[2025,10,12]]},"assertion":[{"value":"2025-12-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}