{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:21:31Z","timestamp":1760955691207,"version":"3.41.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,3,6]],"date-time":"2017-03-06T00:00:00Z","timestamp":1488758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Australian Research Council Discover Project","award":["DP140100104"],"award-info":[{"award-number":["DP140100104"]}]},{"name":"Australian Research Council Linkage Project","award":["LP160100630"],"award-info":[{"award-number":["LP160100630"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>In the era of big data, a mechanism that can automatically annotate disease codes to patients\u2019 records in the medical information system is in demand. The purpose of this work is to propose a framework that automatically annotates the disease labels of multi-source patient data in Intensive Care Units (ICUs). We extract features from two main sources, medical charts and notes. The Bag-of-Words model is used to encode the features. Unlike most of the existing multi-label learning algorithms that globally consider correlations between diseases, our model learns disease correlation locally in the patient data. To achieve this, we derive a local disease correlation representation to enrich the discriminant power of each patient data. This representation is embedded into a unified multi-label learning framework. We develop an alternating algorithm to iteratively optimize the objective function. Extensive experiments have been conducted on a real-world ICU database. We have compared our algorithm with representative multi-label learning algorithms. Evaluation results have shown that our proposed method has state-of-the-art performance in the annotation of multiple diagnostic codes for ICU patients. This study suggests that problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model that exploits disease correlation. The findings of this study will greatly benefit health care and management in ICU considering that the automated diagnosis code annotation can significantly improve the quality and management of health care for both patients and caregivers.<\/jats:p>","DOI":"10.1145\/3003729","type":"journal-article","created":{"date-parts":[[2017,3,7]],"date-time":"2017-03-07T19:12:04Z","timestamp":1488913924000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining"],"prefix":"10.1145","volume":"11","author":[{"given":"Sen","family":"Wang","sequence":"first","affiliation":[{"name":"Griffith University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Queensland, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-8807","authenticated-orcid":false,"given":"Xiaojun","family":"Chang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[{"name":"The University of New South Wales, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquarie University, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Long","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Ultimo NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,3,6]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.5555\/944919.944937"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.1016\/j.patcog.2004.03.009"},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.1109\/TNNLS.2015.2441735"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 1171--1177","author":"Chang Xiaojun","year":"2014","unstructured":"Xiaojun Chang , Feiping Nie , Yi Yang , and Heng Huang . 2014 a. A convex formulation for semi-supervised multi-label feature selection . In Proceedings of the AAAI Conference on Artificial Intelligence. 1171--1177 . Xiaojun Chang, Feiping Nie, Yi Yang, and Heng Huang. 2014a. A convex formulation for semi-supervised multi-label feature selection. In Proceedings of the AAAI Conference on Artificial Intelligence. 1171--1177."},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1007\/978-3-319-06605-9_7"},{"key":"e_1_2_1_6_1","volume-title":"Xing","author":"Chang Xiaojun","year":"2016","unstructured":"Xiaojun Chang , Yao-Liang Yu , Yi Yang , and Eric P . Xing . 2016 . Semantic pooling for complex event analysis in untrimmed videos. IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2016). Xiaojun Chang, Yao-Liang Yu, Yi Yang, and Eric P. Xing. 2016. Semantic pooling for complex event analysis in untrimmed videos. IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1145\/2783258.2783365"},{"volume-title":"Proceedings of the International Conferences on Machine Learning. 279--286","author":"Cheng Weiwei","unstructured":"Weiwei Cheng , Eyke H\u00fcllermeier , and Krzysztof J. Dembczynski . 2010. Bayes optimal multilabel classification via probabilistic classifier chains . In Proceedings of the International Conferences on Machine Learning. 279--286 . Weiwei Cheng, Eyke H\u00fcllermeier, and Krzysztof J. Dembczynski. 2010. Bayes optimal multilabel classification via probabilistic classifier chains. In Proceedings of the International Conferences on Machine Learning. 279--286.","key":"e_1_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1007\/3-540-44794-6_4"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.5555\/1759548.1759554"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 681--687","author":"Elisseeff Andr\u00e9","year":"2001","unstructured":"Andr\u00e9 Elisseeff and Jason Weston . 2001 . A kernel method for multi-labelled classification . In Proceedings of the Advances in Neural Information Processing Systems. 681--687 . Andr\u00e9 Elisseeff and Jason Weston. 2001. A kernel method for multi-labelled classification. In Proceedings of the Advances in Neural Information Processing Systems. 681--687."},{"key":"e_1_2_1_12_1","first-page":"41","article-title":"Multi-task feature learning","volume":"19","author":"Evgeniou A.","year":"2007","unstructured":"A. Evgeniou and Massimiliano Pontil . 2007 . Multi-task feature learning . Advances in Neural Information Processing Systems 19 (2007), 41 -- 48 . A. Evgeniou and Massimiliano Pontil. 2007. Multi-task feature learning. Advances in Neural Information Processing Systems 19 (2007), 41--48.","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"e_1_2_1_13_1","DOI":"10.1197\/jamia.M1552"},{"doi-asserted-by":"publisher","key":"e_1_2_1_14_1","DOI":"10.1007\/s10994-008-5064-8"},{"doi-asserted-by":"publisher","key":"e_1_2_1_15_1","DOI":"10.1145\/2623330.2623742"},{"doi-asserted-by":"publisher","key":"e_1_2_1_16_1","DOI":"10.1145\/2063576.2063734"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 602--609","author":"Huang Sheng-Jun","year":"2012","unstructured":"Sheng-Jun Huang and Zhi-Hua Zhou . 2012 . Multi-label learning by exploiting label correlations locally . In Proceedings of the AAAI Conference on Artificial Intelligence. 602--609 . Sheng-Jun Huang and Zhi-Hua Zhou. 2012. Multi-label learning by exploiting label correlations locally. In Proceedings of the AAAI Conference on Artificial Intelligence. 602--609."},{"volume-title":"Proceedings of the International Conferences on Machine Learning. 543--550","author":"Kim Seyoung","unstructured":"Seyoung Kim and Eric P. Xing . 2010. Tree-guided group lasso for multi-task regression with structured sparsity . In Proceedings of the International Conferences on Machine Learning. 543--550 . Seyoung Kim and Eric P. Xing. 2010. Tree-guided group lasso for multi-task regression with structured sparsity. In Proceedings of the International Conferences on Machine Learning. 543--550.","key":"e_1_2_1_18_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_19_1","DOI":"10.1145\/2487575.2487577"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the International Joint Conference on Natural Language Processing. 877--882","author":"Lita Lucian Vlad","year":"2008","unstructured":"Lucian Vlad Lita , Shipeng Yu , Radu Stefan Niculescu , and Jinbo Bi . 2008 . Large scale diagnostic code classification for medical patient records . In Proceedings of the International Joint Conference on Natural Language Processing. 877--882 . Lucian Vlad Lita, Shipeng Yu, Radu Stefan Niculescu, and Jinbo Bi. 2008. Large scale diagnostic code classification for medical patient records. In Proceedings of the International Joint Conference on Natural Language Processing. 877--882."},{"doi-asserted-by":"publisher","key":"e_1_2_1_21_1","DOI":"10.1109\/TMM.2012.2187179"},{"doi-asserted-by":"publisher","key":"e_1_2_1_22_1","DOI":"10.1145\/1857947.1857950"},{"volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 1813--1821","author":"Nie Feiping","unstructured":"Feiping Nie , Heng Huang , Xiao Cai , and Chris H. Ding . 2010. Efficient and robust feature selection via joint 2, 1-norms minimization . In Proceedings of the Advances in Neural Information Processing Systems. 1813--1821 . Feiping Nie, Heng Huang, Xiao Cai, and Chris H. Ding. 2010. Efficient and robust feature selection via joint 2, 1-norms minimization. In Proceedings of the Advances in Neural Information Processing Systems. 1813--1821.","key":"e_1_2_1_23_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_24_1","DOI":"10.1109\/ICDMW.2011.174"},{"doi-asserted-by":"publisher","key":"e_1_2_1_25_1","DOI":"10.1109\/ICDM.2008.74"},{"doi-asserted-by":"publisher","key":"e_1_2_1_26_1","DOI":"10.1016\/S1386-5056(02)00057-6"},{"doi-asserted-by":"publisher","key":"e_1_2_1_27_1","DOI":"10.1097\/CCM.0b013e31820a92c6"},{"doi-asserted-by":"publisher","key":"e_1_2_1_28_1","DOI":"10.1023\/A:1007649029923"},{"doi-asserted-by":"publisher","key":"e_1_2_1_29_1","DOI":"10.1145\/1644873.1644875"},{"doi-asserted-by":"publisher","key":"e_1_2_1_30_1","DOI":"10.1109\/TKDE.2010.164"},{"doi-asserted-by":"publisher","key":"e_1_2_1_31_1","DOI":"10.1007\/978-3-540-74958-5_38"},{"doi-asserted-by":"publisher","key":"e_1_2_1_32_1","DOI":"10.1145\/2339530.2339605"},{"doi-asserted-by":"publisher","key":"e_1_2_1_33_1","DOI":"10.1109\/TBME.2014.2358632"},{"doi-asserted-by":"publisher","key":"e_1_2_1_34_1","DOI":"10.5555\/2354409.2355019"},{"doi-asserted-by":"publisher","key":"e_1_2_1_35_1","DOI":"10.1093\/bioinformatics\/btv212"},{"doi-asserted-by":"publisher","key":"e_1_2_1_36_1","DOI":"10.1016\/j.patcog.2015.01.022"},{"doi-asserted-by":"publisher","key":"e_1_2_1_37_1","DOI":"10.1109\/ICDM.2012.23"},{"doi-asserted-by":"publisher","key":"e_1_2_1_38_1","DOI":"10.1093\/bioinformatics\/btt320"},{"doi-asserted-by":"publisher","key":"e_1_2_1_39_1","DOI":"10.1109\/TBME.2014.2329753"},{"doi-asserted-by":"publisher","key":"e_1_2_1_40_1","DOI":"10.1145\/1835804.1835831"},{"doi-asserted-by":"publisher","key":"e_1_2_1_41_1","DOI":"10.1109\/TMM.2012.2237023"},{"doi-asserted-by":"publisher","key":"e_1_2_1_42_1","DOI":"10.1007\/s11263-014-0781-x"},{"doi-asserted-by":"publisher","key":"e_1_2_1_43_1","DOI":"10.1109\/TPAMI.2011.170"},{"doi-asserted-by":"publisher","key":"e_1_2_1_44_1","DOI":"10.1145\/1342320.1342324"},{"doi-asserted-by":"publisher","key":"e_1_2_1_45_1","DOI":"10.1007\/s11063-009-9095-3"},{"doi-asserted-by":"publisher","key":"e_1_2_1_46_1","DOI":"10.5555\/2283516.2283663"},{"doi-asserted-by":"publisher","key":"e_1_2_1_47_1","DOI":"10.1016\/j.patcog.2006.12.019"},{"doi-asserted-by":"publisher","key":"e_1_2_1_48_1","DOI":"10.1109\/TKDE.2013.39"},{"doi-asserted-by":"publisher","key":"e_1_2_1_49_1","DOI":"10.1109\/TCYB.2015.2403356"},{"doi-asserted-by":"publisher","key":"e_1_2_1_50_1","DOI":"10.1109\/TBME.2015.2466616"},{"key":"e_1_2_1_51_1","volume-title":"Alzheimers Disease Neuroimaging Initiative, et al","author":"Zhu Xiaofeng","year":"2015","unstructured":"Xiaofeng Zhu , Heung-Il Suk , Li Wang , Seong-Whan Lee , Dinggang Shen , Alzheimers Disease Neuroimaging Initiative, et al . 2015 . A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Medical Image Analysis ( 2015). Xiaofeng Zhu, Heung-Il Suk, Li Wang, Seong-Whan Lee, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative, et al. 2015. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Medical Image Analysis (2015)."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3003729","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3003729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:49:55Z","timestamp":1750218595000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3003729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,6]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,8,31]]}},"alternative-id":["10.1145\/3003729"],"URL":"https:\/\/doi.org\/10.1145\/3003729","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2017,3,6]]},"assertion":[{"value":"2015-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2016-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-03-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}