{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:11Z","timestamp":1785340751776,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819467","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Fusion and Adaptive Learning for Cardiopulmonary Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6190-0022","authenticated-orcid":false,"given":"Mahjabeen Tamanna","family":"Abed","sequence":"first","affiliation":[{"name":"Washington State University, Vancouver, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5120-0972","authenticated-orcid":false,"given":"Xinghui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Washington State University, Vancouver, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"John Arevalo Thamar Solorio Manuel Montes-y G\u00f3mez and Fabio\u00a0A Gonz\u00e1lez. 2017. Gated multimodal units for information fusion. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1702.01992 (2017)."},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Tadas Baltru\u0161aitis Chaitanya Ahuja and Louis-Philippe Morency. 2018. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence 41 2 (2018) 423\u2013443.","DOI":"10.1109\/TPAMI.2018.2798607"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Meha Desai and Manan Shah. 2021. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth 4 (2021) 1\u201311.","DOI":"10.1016\/j.ceh.2020.11.002"},{"key":"e_1_3_3_1_5_2","unstructured":"Brian Gow Tom Pollard Larry\u00a0A Nathanson Alistair Johnson Benjamin Moody Chrystinne Fernandes Nathaniel Greenbaum Jonathan\u00a0W Waks Parastou Eslami Tanner Carbonati et\u00a0al. 2023. Mimic-iv-ecg: Diagnostic electrocardiogram matched subset. Type: dataset 6 (2023) 13\u201314."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.2005.1571679"},{"key":"e_1_3_3_1_7_2","first-page":"1321","volume-title":"International conference on machine learning","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian\u00a0Q Weinberger. 2017. On calibration of modern neural networks. In International conference on machine learning. PMLR, 1321\u20131330."},{"key":"e_1_3_3_1_8_2","unstructured":"Geoffrey Hinton Oriol Vinyals and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1503.02531 (2015)."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Sarah Jabbour David Fouhey Ella Kazerooni Jenna Wiens and Michael\u00a0W Sjoding. 2022. Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. Journal of the American Medical Informatics Association 29 6 (2022) 1060\u20131068.","DOI":"10.1093\/jamia\/ocac030"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Alistair\u00a0EW Johnson Lucas Bulgarelli Lu Shen Alvin Gayles Ayad Shammout Steven Horng Tom\u00a0J Pollard Sicheng Hao Benjamin Moody Brian Gow et\u00a0al. 2023. MIMIC-IV a freely accessible electronic health record dataset. Scientific data 10 1 (2023) 1.","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Alistair\u00a0EW Johnson Tom\u00a0J Pollard Nathaniel\u00a0R Greenbaum Matthew\u00a0P Lungren Chih-ying Deng Yifan Peng Zhiyong Lu Roger\u00a0G Mark Seth\u00a0J Berkowitz and Steven Horng. 2019. MIMIC-CXR-JPG a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1901.07042 (2019).","DOI":"10.1038\/s41597-019-0322-0"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Adrienne Kline Hanyin Wang Yikuan Li Saya Dennis Meghan Hutch Zhenxing Xu Fei Wang Feixiong Cheng and Yuan Luo. 2022. Multimodal machine learning in precision health: A scoping review. NPJ digital medicine 5 1 (2022) 171.","DOI":"10.1038\/s41746-022-00712-8"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Felix Krones Umar Marikkar Guy Parsons Adam Szmul and Adam Mahdi. 2025. Review of multimodal machine learning approaches in healthcare. Information Fusion 114 (2025) 102690.","DOI":"10.1016\/j.inffus.2024.102690"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Chih-Kuo Lee Ting-Li Chen Jeng-En Wu Min-Tsun Liao Chiehhung Wang Weichung Wang and Cheng-Ying Chou. 2024. Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department. Computer Methods and Programs in Biomedicine 255 (2024) 108357.","DOI":"10.1016\/j.cmpb.2024.108357"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Pang-Yen Liu Shi-Chue Hsing Dung-Jang Tsai Chin Lin Chin-Sheng Lin Chih-Hung Wang and Wen-Hui Fang. 2025. A deep-learning-enabled electrocardiogram and chest X-ray for detecting pulmonary arterial hypertension. Journal of Imaging Informatics in Medicine 38 2 (2025) 747\u2013756.","DOI":"10.1007\/s10278-024-01225-4"},{"key":"e_1_3_3_1_17_2","unstructured":"Farida Mohsen and Zubair Shah. 2025. Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2504.05338 (2025)."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Natalia Neverova Christian Wolf Graham Taylor and Florian Nebout. 2015. Moddrop: adaptive multi-modal gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 38 8 (2015) 1692\u20131706.","DOI":"10.1109\/TPAMI.2015.2461544"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Makoto Nishimori Kunihiko Kiuchi Kunihiro Nishimura Kengo Kusano Akihiro Yoshida Kazumasa Adachi Yasutaka Hirayama Yuichiro Miyazaki Ryudo Fujiwara Philipp Sommer et\u00a0al. 2021. Accessory pathway analysis using a multimodal deep learning model. Scientific Reports 11 1 (2021) 8045.","DOI":"10.1038\/s41598-021-87631-y"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Yanmin Qian Mengxiao Bi Tian Tan and Kai Yu. 2016. Very deep convolutional neural networks for noise robust speech recognition. IEEE\/ACM Transactions on Audio Speech and Language Processing 24 12 (2016) 2263\u20132276.","DOI":"10.1109\/TASLP.2016.2602884"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Adriel Saporta Aahlad\u00a0Manas Puli Mark Goldstein and Rajesh Ranganath. 2024. Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. Advances in Neural Information Processing Systems 37 (2024) 56919\u201356957.","DOI":"10.52202\/079017-1814"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/1101149.1101236"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Jen\u00a0Hong Tan Yuki Hagiwara Winnie Pang Ivy Lim Shu\u00a0Lih Oh Muhammad Adam Ru San\u00a0Tan Ming Chen and U\u00a0Rajendra Acharya. 2018. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in biology and medicine 94 (2018) 19\u201326.","DOI":"10.1016\/j.compbiomed.2017.12.023"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Shamik Tiwari Anurag Jain Varun Sapra Deepika Koundal Fayadh Alenezi Kemal Polat Adi Alhudhaif and Majid Nour. 2023. A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model. Expert Systems with Applications 213 (2023) 118933.","DOI":"10.1016\/j.eswa.2022.118933"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Marly Van\u00a0Assen Amara Tariq Alexander\u00a0C Razavi Carl Yang Imon Banerjee and Carlo\u00a0N De\u00a0Cecco. 2023. Fusion modeling: combining clinical and imaging data to advance cardiac care. Circulation: Cardiovascular Imaging 16 12 (2023) e014533.","DOI":"10.1161\/CIRCIMAGING.122.014533"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Elisa Warner Joonsang Lee William Hsu Tanveer Syeda-Mahmood Charles\u00a0E Kahn\u00a0Jr Olivier Gevaert and Arvind Rao. 2024. Multimodal machine learning in image-based and clinical biomedicine: Survey and prospects. International journal of computer vision 132 9 (2024) 3753\u20133769.","DOI":"10.1007\/s11263-024-02032-8"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Lizhong Wu Sharon\u00a0L. Oviatt and Philip\u00a0R. Cohen. 1999. Multimodal integration-a statistical view. IEEE Transactions on Multimedia 1 4 (1999) 334\u2013341.","DOI":"10.1109\/6046.807953"}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819467","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:12:31Z","timestamp":1785337951000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":26,"alternative-id":["10.1145\/3807503.3819467","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819467","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}