{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:53:19Z","timestamp":1765389199009,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":23,"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":["2316003"],"award-info":[{"award-number":["2316003"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013759","name":"Commonwealth Health Research Board","doi-asserted-by":"publisher","award":["2360623"],"award-info":[{"award-number":["2360623"]}],"id":[{"id":"10.13039\/100013759","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009238","name":"Virginia Commonwealth University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009238","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH (National Institutes of Health)","doi-asserted-by":"publisher","award":["1S10OD038214-01","5R21MH128562-02","5R21AA029492-02"],"award-info":[{"award-number":["1S10OD038214-01","5R21MH128562-02","5R21AA029492-02"]}],"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\/3765612.3767211","type":"proceedings-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:45:59Z","timestamp":1765388759000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ICU-Length of Stay Prediction on Electronic Health Records using Graph Neural Networks and Homogeneous Similarity Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5508-9968","authenticated-orcid":false,"given":"Ahmad F.","family":"Al Musawi","sequence":"first","affiliation":[{"name":"Virginia Commonwealth University, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9199-2479","authenticated-orcid":false,"given":"Pratip","family":"Rana","sequence":"additional","affiliation":[{"name":"Old Dominion University, Norfolk, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0281-8855","authenticated-orcid":false,"given":"Sibtanu","family":"Raha","sequence":"additional","affiliation":[{"name":"EAB, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6628-4267","authenticated-orcid":false,"given":"Joshua","family":"Braunstein","sequence":"additional","affiliation":[{"name":"US Department of Veterans Affairs, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8431-8483","authenticated-orcid":false,"suffix":"IV","given":"William C.","family":"Sleeman","sequence":"additional","affiliation":[{"name":"US Department of Veterans Affairs, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3064-4088","authenticated-orcid":false,"given":"Rishabh","family":"Kapoor","sequence":"additional","affiliation":[{"name":"US Department of Veterans Affairs, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3880-5886","authenticated-orcid":false,"given":"Preetam","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Virginia Commonwealth University, Richmond, VIRGINIA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/app142210523"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Belal Alsinglawi Osama Alshari Mohammed Alorjani Omar Mubin Fady Alnajjar Mauricio Novoa and Omar Darwish. 2022. An explainable machine learning framework for lung cancer hospital length of stay prediction. Scientific reports 12 1 607.","DOI":"10.1038\/s41598-021-04608-7"},{"key":"e_1_3_2_1_3_1","unstructured":"Helo\u00edsa Oss Boll Ali Amirahmadi Amira Soliman Stefan Byttner and Mariana Recamonde Mendoza. 2024. Graph neural networks for heart failure prediction on an ehr-based patient similarity graph. ArXiv abs\/2411.19742. https:\/\/api.semanticscholar.org\/CorpusID:274422701."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105678"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Sunyang Fu et al. 2020. Assessment of the impact of ehr heterogeneity for clinical research through a case study of silent brain infarction. BMC medical informatics and decision making 20 1\u201312.","DOI":"10.1186\/s12911-020-1072-9"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"D Geethamani and R Rangaraj. [n. d.] Feature augmented stacked bagging for length of stay prediction on mimic iii. International Journal of Health Sciences II 8742\u20138751.","DOI":"10.53730\/ijhs.v6nS2.7261"},{"key":"e_1_3_2_1_7_1","volume-title":"3rd intl conf on big data intelligence and computing and cyber science and technology congress (DASC\/PiCom\/DataCom\/CyberSciTech)","author":"Gentimis Thanos","unstructured":"Thanos Gentimis, Alnaser Ala'J, Alex Durante, Kyle Cook, and Robert Steele. 2017. Predicting hospital length of stay using neural networks on mimic iii data. In 2017 IEEE 15th intl conf on dependable, autonomic and secure computing, 15th intl conf on pervasive intelligence and computing, 3rd intl conf on big data intelligence and computing and cyber science and technology congress (DASC\/PiCom\/DataCom\/CyberSciTech). IEEE, 1194\u20131201."},{"key":"e_1_3_2_1_8_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CAI54212.2023.00142"},{"key":"e_1_3_2_1_10_1","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Iryna Manuilova et al. 2024. Identifications of similarity metrics for patients with cancer: protocol for a scoping review. JMIR Research Protocols 13. https:\/\/api.semanticscholar.org\/CorpusID:271264257.","DOI":"10.2196\/58705"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-024-00169-2"},{"key":"e_1_3_2_1_13_1","unstructured":"Anubhav Reddy Nallabasannagari Madhu Reddiboina Ryan Seltzer Trevor Zeffiro Ajay Sharma and Mahendra Bhandari. 2020. All data inclusive deep learning models to predict critical events in the medical information mart for intensive care iii database (mimic iii). arXiv preprint arXiv:2009.01366."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.04.007"},{"key":"e_1_3_2_1_15_1","first-page":"2071","article-title":"Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks","volume":"27","author":"Tang Siyi","year":"2023","unstructured":"Siyi Tang, Amara Tariq, Jared A Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L Rubin, Bhavik N Patel, and Imon Banerjee. 2023. Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks. IEEE Journal of Biomedical and Health Informatics, 27, 4, 2071\u20132082.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi15090304"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Brandon Theodorou Cao Xiao and Jimeng Sun. 2023. Synthesize high dimensional longitudinal electronic health records via hierarchical autoregressive language model. Nature communications 14 1 5305.","DOI":"10.1038\/s41467-023-41093-0"},{"key":"e_1_3_2_1_18_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint. arXiv: 1710.10903."},{"key":"e_1_3_2_1_19_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826."},{"key":"e_1_3_2_1_20_1","unstructured":"Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? Advances in neural information processing systems 34 28877\u201328888."},{"key":"e_1_3_2_1_21_1","unstructured":"Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph transformer networks. Advances in neural information processing systems 32."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIBCB.2019.8791477"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Dongdong Zhang Changchang Yin Jucheng Zeng Xiaohui Yuan and Ping Zhang. 2020. Combining structured and unstructured data for predictive models: a deep learning approach. BMC medical informatics and decision making 20 1\u201311.","DOI":"10.1186\/s12911-020-01297-6"}],"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\/3765612.3767211","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765612.3767211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T17:48:59Z","timestamp":1765388939000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3765612.3767211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,12]]},"references-count":23,"alternative-id":["10.1145\/3765612.3767211","10.1145\/3765612"],"URL":"https:\/\/doi.org\/10.1145\/3765612.3767211","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"}}]}}