{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:10:04Z","timestamp":1755821404038,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973069, 62106003, 2022A1515011474, 62102265"],"award-info":[{"award-number":["61973069, 62106003, 2022A1515011474, 62102265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)","award":["GML-KF-22-29"],"award-info":[{"award-number":["GML-KF-22-29"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612564","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:26:54Z","timestamp":1698391614000},"page":"5428-5435","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1841-539X","authenticated-orcid":false,"given":"Zhenghan","family":"Chen","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1083-9486","authenticated-orcid":false,"given":"Changzeng","family":"Fu","sequence":"additional","affiliation":[{"name":"Northeastern University, Qinhuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1639-342X","authenticated-orcid":false,"given":"Ruoxue","family":"Wu","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-3892","authenticated-orcid":false,"given":"Ye","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6377-0884","authenticated-orcid":false,"given":"Xunzhu","family":"Tang","sequence":"additional","affiliation":[{"name":"University of Luxemburg, Luxemburg, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8041-2798","authenticated-orcid":false,"given":"Xiaoxuan","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Workshops at the thirty-second AAAI conference on artificial intelligence.","author":"Baumel Tal","year":"2018","unstructured":"Tal Baumel, Jumana Nassour-Kassis, Raphael Cohen, Michael Elhadad, and No\u00e9mie Elhadad. 2018. Multi-label classification of patient notes: case study on icd code assignment. In Workshops at the thirty-second AAAI conference on artificial intelligence."},{"key":"e_1_3_2_1_2_1","unstructured":"Antoine Bordes Nicolas Usunier Alberto Garcia-Duran JasonWeston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.282"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.282"},{"key":"e_1_3_2_1_5_1","unstructured":"Niel Chah. 2017. Freebase-triples: a methodology for processing the freebase data dumps. arXiv preprint arXiv:1712.08707."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9050750"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/288627.288649"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI","author":"Pajak Maciej","year":"2019","unstructured":"Mat\u00fa? Falis, Maciej Pajak, Aneta Lisowska, Patrick Schrempf, Lucas Deckers, Shadia Mikhael, Sotirios Tsaftaris, and Alison O'Neil. 2019. Ontological attention ensembles for capturing semantic concepts in icd code prediction from clinical text. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), 168--177."},{"key":"e_1_3_2_1_9_1","unstructured":"Shaoxiong Ji Erik Cambria and Pekka Marttinen. 2020. Dilated convolutional attention network for medical code assignment from clinical text. arXiv preprint arXiv:2009.14578."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Alistair EW Johnson et al. 2016. Mimic-iii a freely accessible critical care database. Scientific data 3 1 1--9.","DOI":"10.1038\/sdata.2016.35"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6331"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.184"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1100"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"James Mullenbach Sarah Wiegreffe Jon Duke Jimeng Sun and Jacob Eisenstein. 2018. Explainable prediction of medical codes from clinical text. arXiv preprint arXiv:1802.05695.","DOI":"10.18653\/v1\/N18-1100"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002159"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298044"},{"key":"e_1_3_2_1_18_1","unstructured":"Najmeh Sadoughi Greg P Finley James Fone Vignesh Murali Maxim Korenevski Slava Baryshnikov Nico Axtmann Mark Miller and David Suendermann-Oeft. 2018. Medical code prediction with multi-view convolution and description regularized label-dependent attention. arXiv preprint arXiv:1811.01468."},{"volume-title":"European semantic web conference","author":"Schlichtkrull Michael","key":"e_1_3_2_1_19_1","unstructured":"Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593--607."},{"key":"e_1_3_2_1_20_1","unstructured":"Haoran Shi Pengtao Xie Zhiting Hu Ming Zhang and Eric P Xing. 2017. Towards automated icd coding using deep learning. arXiv preprint arXiv:1711.04075."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Aaron Sonabend Winston Cai Yuri Ahuja Ashwin Ananthakrishnan Zongqi Xia Sheng Yu and Chuan Hong. 2020. Automated icd coding via unsupervised knowledge integration (unite). International journal of medical informatics 139 104135.","DOI":"10.1016\/j.ijmedinf.2020.104135"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67256-4_4"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5354"},{"volume-title":"Deep patient similarity learning for personalized healthcare","author":"Suo Qiuling","key":"e_1_3_2_1_24_1","unstructured":"Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, and Aidong Zhang. 2018. Deep patient similarity learning for personalized healthcare. IEEE transactions on nanobioscience, 17, 3, 219--227."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Reed T Sutton David Pincock Daniel C Baumgart Daniel C Sadowski Richard N Fedorak and Karen I Kroeker. 2020. An overview of clinical decision support systems: benefits risks and strategies for success. NPJ digital medicine 3 1 17.","DOI":"10.1038\/s41746-020-0221-y"},{"key":"e_1_3_2_1_26_1","volume-title":"Dat Quoc Nguyen, and Anthony Nguyen","author":"Vu Thanh","year":"2020","unstructured":"Thanh Vu, Dat Quoc Nguyen, and Anthony Nguyen. 2020. A label attention model for icd coding from clinical text. arXiv preprint arXiv:2007.06351."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1216"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401135"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-92310--5"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00126-0_38"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2019.00205"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3469470"},{"volume-title":"A comprehensive survey on graph neural networks","author":"Wu Zonghan","key":"e_1_3_2_1_33_1","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32, 1, 4--24."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1098"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357897"},{"key":"e_1_3_2_1_36_1","unstructured":"Keyang Xu et al. 2019. Multimodal machine learning for automated icd coding. In Machine learning for healthcare conference. PMLR 197--215."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Zheng Yuan Chuanqi Tan and Songfang Huang. 2022. Code synonyms do matter: multiple synonyms matching network for automatic icd coding. arXiv preprint arXiv:2203.01515.","DOI":"10.18653\/v1\/2022.acl-short.91"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.463"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612564","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612564","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:57:39Z","timestamp":1755820659000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":40,"alternative-id":["10.1145\/3581783.3612564","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612564","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}