{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:33:31Z","timestamp":1768476811356,"version":"3.49.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T00:00:00Z","timestamp":1523404800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2018,5]]},"DOI":"10.1007\/s10916-018-0951-4","type":"journal-article","created":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T06:09:38Z","timestamp":1523426978000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding"],"prefix":"10.1007","volume":"42","author":[{"given":"Dang","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,4,11]]},"reference":[{"key":"951_CR1","unstructured":"World Health Organization: International Classification of Diseases (ICD). \n                    http:\/\/www.who.int\/classifications\/icd\/en\/\n                    \n                  , 2013"},{"key":"951_CR2","unstructured":"World Health Organization: International statistical classification of diseases and related health problems 10th revision. [Online]. Available: \n                    http:\/\/apps.who.int\/classifications\/icd10\/browse\/2010\/en\n                    \n                  , 2010"},{"key":"951_CR3","unstructured":"Australian Consortium for Classification Development: ICD-10-AM. [Online]. Available: \n                    https:\/\/www.accd.net.au\/Icd10.aspx\n                    \n                  , 2017"},{"key":"951_CR4","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1111\/j.1475-6773.2005.00444.x","volume":"40","author":"K O\u2019Malley","year":"2005","unstructured":"O\u2019Malley, K., Cook, K., Price, M., Wildes, K. R., Hurdle, J., and Ashton, C., Measuring diagnoses: ICD code accuracy. Health Serv. Res. 40:1620\u20131639, 2005.","journal-title":"Health Serv. Res."},{"key":"951_CR5","unstructured":"Wang, F., Hu, J., and Sun, J.: Medical prognosis based on patient similarity and expert feedback. In: The 21st International Conference on Pattern Recognition, pp. 1799\u20131802, IEEE, 2012."},{"key":"951_CR6","unstructured":"Choi, E., Schuetz, A., Stewart, W. F., and Sun, J.: Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv:\n                    1602.03686\n                    \n                  , 2016"},{"key":"951_CR7","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111\u20133119, 2013."},{"issue":"5","key":"951_CR8","doi-asserted-by":"publisher","first-page":"e0127428","DOI":"10.1371\/journal.pone.0127428","volume":"10","author":"J Lee","year":"2015","unstructured":"Lee, J., Maslove, D.M., and Dubin, J., Personalized mortality prediction driven by electronic medical data and a patient similarity metric. PloS One 10(5):e0127428, 2015.","journal-title":"PloS One"},{"issue":"5","key":"951_CR9","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.apmr.2010.01.013","volume":"91","author":"G Carnaby-Mann","year":"2010","unstructured":"Carnaby-Mann, G., and Crary, M., Mcneill dysphagia therapy program: a case-control study. Arch. Phys. Med. Rehabil. 91(5):743\u2013749, 2010.","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"951_CR10","unstructured":"Hielscher, T., Spiliopoulou, M., V\u00f6lzke, H., and K\u00fchn, J.-P.: Using participant similarity for the classification of epidemiological data on hepatic steatosis. In: The 27th International Symposium on Computer-Based Medical Systems, pp. 1\u20137, IEEE, 2014."},{"key":"951_CR11","unstructured":"Le, Q, and Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188\u20131196, 2014."},{"key":"951_CR12","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1162\/tacl_a_00134","volume":"3","author":"O Levy","year":"2015","unstructured":"Levy, O., Goldberg, Y., and Dagan, I., Improving distributional similarity with lessons learned from word embeddings. Trans. Assoc. Comput. Linguist. 3:211\u2013225, 2015.","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"951_CR13","unstructured":"Grover, A, and Leskovec, J.: node2vec: scalable feature learning for networks in KDD. In: ACM, pp. 855\u2013864, 2016."},{"key":"951_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Luo, W., Nguyen, T. D., Venkatesh, S., and Phung, D.: Learning graph representation via frequent subgraphs. In: SDM. Accepted, SIAM, 2018.","DOI":"10.1137\/1.9781611975321.35"},{"issue":"2","key":"951_CR15","first-page":"1","volume":"15","author":"H Moen","year":"2015","unstructured":"Moen, H., Ginter, F., Marsi, E., Peltonen, L.-M., Salakoski, T., and Salanter\u00e4, S., Care episode retrieval: distributional semantic models for information retrieval in the clinical domain. BMC Med. Inform. Decis. Mak. 15(2):1, 2015.","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"1","key":"951_CR16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","volume":"21","author":"P Nguyen","year":"2017","unstructured":"Nguyen, P., Tran, T., Wickramasinghe, N., and Venkatesh, S., Deepr: a convolutional net for medical records. IEEE J. Biomed. Health Inform. 21(1):22\u201330, 2017.","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"951_CR17","unstructured":"Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., Tejedor-Sojo, J., and Sun. J.: Multi-layer representation learning for medical concepts in KDD. In: ACM, pp. 1495\u20131504, 2016."},{"key":"951_CR18","unstructured":"Choi, Y., Chiu, C. Y.-I., and Sontag, D.: Learning low-dimensional representations of medical concepts. In: AMIA Summits on Translational Science Proceedings, pp. 41\u201351, 2016."},{"key":"951_CR19","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J.: Efficient estimation of word representations in vector space. arXiv:\n                    1301.3781\n                    \n                  , 2013"},{"key":"951_CR20","doi-asserted-by":"publisher","first-page":"i969","DOI":"10.1136\/bmj.i969","volume":"352","author":"N Pearce","year":"2016","unstructured":"Pearce, N., Analysis of matched case-control studies. BMJ 352:i969, 2016.","journal-title":"BMJ"},{"key":"951_CR21","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Luo, W., Phung, D., and Venkatesh, S.: Exceptional contrast set mining: moving beyond the deluge of the obvious. In: Australasian Joint Conference on Artificial Intelligence, pp. 455\u2013468. Springer, Berlin, 2016.","DOI":"10.1007\/978-3-319-50127-7_39"},{"issue":"5","key":"951_CR22","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1147\/JRD.2011.2160684","volume":"55","author":"J Bigus","year":"2011","unstructured":"Bigus, J., Campbell, M., Carmeli, B., Cefkin, M., Chang, H., Chen-Ritzo, C.-H., Cody, W., Ebadollahi, S., Evfimievski, A., Farkash, A., et al., Information technology for healthcare transformation. IBM Journal of Research and Development 55(5):6\u201320, 2011.","journal-title":"IBM Journal of Research and Development"},{"issue":"11","key":"951_CR23","first-page":"e523","volume":"20","author":"K Thomas","year":"2014","unstructured":"Thomas, K., Rahman, M., Mor, V., and Intrator, O., Influence of hospital and nursing home quality on hospital readmissions. The American Journal of Managed Care 20(11):e523, 2014.","journal-title":"The American Journal of Managed Care"},{"issue":"1","key":"951_CR24","doi-asserted-by":"publisher","first-page":"117","DOI":"10.11124\/JBISRIR-2017-003386","volume":"16","author":"S H\u00e5konsen","year":"2018","unstructured":"H\u00e5konsen, S., Pedersen, P., Bjerrum, M., Bygholm, A., and Peters, M., Nursing minimum data sets for documenting nutritional care for adults in primary healthcare: a scoping review. JBI Database of Systematic Reviews and Implementation Reports 16(1):117\u2013139, 2018.","journal-title":"JBI Database of Systematic Reviews and Implementation Reports"},{"key":"951_CR25","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L. V. D., and Hinton, G., Visualizing data using t-sne. Journal of Machine Learning Research 9: 2579\u20132605, 2008.","journal-title":"Journal of Machine Learning Research"},{"key":"951_CR26","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","volume":"56","author":"J Futoma","year":"2015","unstructured":"Futoma, J., Morris, J., and Lucas, J., A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics 56:229\u2013238, 2015.","journal-title":"Journal of Biomedical Informatics"},{"key":"951_CR27","first-page":"30","volume-title":"Deepcare: a deep dynamic memory model for predictive medicine in PAKDD","author":"T Pham","year":"2016","unstructured":"Pham, T., Tran, T., Phung, D., and Venkatesh, S., Deepcare: a deep dynamic memory model for predictive medicine in PAKDD, pp. 30\u201341. Berlin: Springer, 2016."},{"key":"951_CR28","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.eswa.2017.02.023","volume":"78","author":"L Turgeman","year":"2017","unstructured":"Turgeman, L., May, J., and Sciulli, R., Insights from a machine learning model for predicting the hospital length of stay (los) at the time of admission. Expert Systems with Applications 78:376\u2013385, 2017.","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"951_CR29","doi-asserted-by":"publisher","first-page":"e0165756","DOI":"10.1371\/journal.pone.0165756","volume":"12","author":"C-H Chaou","year":"2017","unstructured":"Chaou, C.-H., Chen, H.-H., Chang, S.-H., Tang, P., Pan, S.-L., Yen, A. M.-F., and Chiu, T.-F., Predicting length of stay among patients discharged from the emergency departmentusing an accelerated failure time model. PloS One 12(1):e0165756, 2017.","journal-title":"PloS One"},{"key":"951_CR30","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Nguyen, T. D., Luo, W., and Venkatesh, S.: Trans2vec: learning transaction embedding via items and frequent itemsets. In: PAKDD. Accepted. Springer, Berlin, 2018.","DOI":"10.1007\/978-3-319-93040-4_29"},{"issue":"2","key":"951_CR31","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/s10489-015-0657-y","volume":"44","author":"N Pobiedina","year":"2016","unstructured":"Pobiedina, N., and Ichise, R., Citation count prediction as a link prediction problem. Applied Intelligence 44(2):252\u2013268, 2016.","journal-title":"Applied Intelligence"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-018-0951-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-0951-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-0951-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T23:26:16Z","timestamp":1554938776000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-018-0951-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,11]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,5]]}},"alternative-id":["951"],"URL":"https:\/\/doi.org\/10.1007\/s10916-018-0951-4","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,11]]},"assertion":[{"value":"14 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Ethics approval was obtained from the New South Wales Population and Health Services Research Ethics Committee (AU RED Reference: HREC\/15\/CIPHS\/1).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"This study is a secondary analysis of routinely collected data, and the consent had been obtained by the original data guarantor.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"94"}}