{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:18:39Z","timestamp":1744345119693},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"S14","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Laboratory indicator test results in electronic health records have been applied to many clinical big data analysis. However, it is quite common that the same laboratory examination item (i.e., lab indicator) is presented using different names in Chinese due to the translation problem and the habit problem of various hospitals, which results in distortion of analysis results.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A framework with a recall model and a binary classification model is proposed, which could reduce the alignment scale and improve the accuracy of lab indicator normalization. To reduce alignment scale, tf-idf is used for candidate selection. To assure the accuracy of output, we utilize enhanced sequential inference model for binary classification. And active learning is applied with a selection strategy which is proposed for reducing annotation cost.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Since our indicator standardization method mainly focuses on Chinese indicator inconsistency, we perform our experiment on Shanghai Hospital Development Center and select clinical data from 8 hospitals. The method achieves a F1-score 92.08<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in our final binary classification. As for active learning, the new strategy proposed performs better than random baseline and could outperform the result trained on full data with only 43<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> training data. A case study on heart failure clinic analysis conducted on the sub-dataset collected from SHDC shows that our proposed method is practical in the application with good performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This work demonstrates that the structure we proposed can be effectively applied to lab indicator normalization. And active learning is also suitable for this task for cost reduction. Such a method is also valuable in data cleaning, data mining, text extracting and entity alignment.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-01324-6","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T17:04:06Z","timestamp":1608051846000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Lab indicators standardization method for the regional healthcare platform: a case study on heart failure"],"prefix":"10.1186","volume":"20","author":[{"given":"Ming","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZhiXing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JiaYing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"issue":"suppl\u20131","key":"1324_CR1","first-page":"A15577","volume":"136","author":"S Arora","year":"2017","unstructured":"Arora S, Caughey MC, Misenheimer JA, Jones WM, Fish AC, Smith SC Jr, Stouffer GA, Kaul P. Elevated serum aspartate transaminase as a predictor of early mortality in patients with non-ST-segment elevation myocardial infarction. Circulation. 2017;136(suppl\u20131):A15577.","journal-title":"Circulation"},{"key":"1324_CR2","doi-asserted-by":"crossref","unstructured":"Rong S, Niu X, Xiang EW, Wang H, Yang Q, Yu Y. A machine learning approach for instance matching based on similarity metrics. In: International semantic web conference. Springer, pp 460\u2013475; 2012.","DOI":"10.1007\/978-3-642-35176-1_29"},{"issue":"1","key":"1324_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2007.250581","volume":"19","author":"AK Elmagarmid","year":"2006","unstructured":"Elmagarmid AK, Ipeirotis PG, Verykios VS. Duplicate record detection: a survey. IEEE Trans Knowl Data Eng. 2006;19(1):1\u201316.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"1324_CR4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MIS.2003.1234765","volume":"18","author":"M Bilenko","year":"2003","unstructured":"Bilenko M, Mooney R, Cohen W, Ravikumar P, Fienberg S. Adaptive name matching in information integration. IEEE Intell Syst. 2003;18(5):16\u201323.","journal-title":"IEEE Intell Syst"},{"issue":"3","key":"1324_CR5","doi-asserted-by":"publisher","first-page":"157","DOI":"10.14778\/2078331.2078332","volume":"5","author":"FM Suchanek","year":"2011","unstructured":"Suchanek FM, Abiteboul S, Senellart P. Paris: probabilistic alignment of relations, instances, and schema. Proc VLDB Endow. 2011;5(3):157\u201368.","journal-title":"Proc VLDB Endow"},{"issue":"1","key":"1324_CR6","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11704-017-6561-3","volume":"13","author":"C Kong","year":"2019","unstructured":"Kong C, Gao M, Xu C, Fu Y, Qian W, Zhou A. Enali: entity alignment across multiple heterogeneous data sources. Front Comput Sci. 2019;13(1):157\u201369.","journal-title":"Front Comput Sci"},{"issue":"1","key":"1324_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.datak.2008.06.003","volume":"67","author":"W Hu","year":"2008","unstructured":"Hu W, Qu Y, Cheng G. Matching large ontologies: a divide-and-conquer approach. Data Knowl Eng. 2008;67(1):140\u201360.","journal-title":"Data Knowl Eng"},{"key":"1324_CR8","unstructured":"Wang Z, Li J, Tang J. Boosting cross-lingual knowledge linking via concept annotation. In: Proceedings of the 23rd international joint conference on artificial intelligence. IJCAI, pp 2733\u20132739; 2013"},{"issue":"3","key":"1324_CR9","first-page":"701","volume":"40","author":"X Wang","year":"2017","unstructured":"Wang X, Liu K, He S, Liu S, Zhang Y, Zhao J. Multi-source knowledge bases entity alignment by leveraging semantic tags. Jisuanji Xuebao\/Chin J Comput. 2017;40(3):701\u201311.","journal-title":"Jisuanji Xuebao\/Chin J Comput"},{"issue":"1","key":"1324_CR10","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13326-017-0145-x","volume":"8","author":"T Ruan","year":"2017","unstructured":"Ruan T, Wang M, Sun J, Wang T, Zeng L, Yin Y, Gao J. An automatic approach for constructing a knowledge base of symptoms in Chinese. J Biomed Semant. 2017;8(1):33.","journal-title":"J Biomed Semant"},{"key":"1324_CR11","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-319-12277-9_4","volume-title":"Chinese computational linguistics and natural language processing based on naturally annotated big data","author":"Y Zhang","year":"2014","unstructured":"Zhang Y, Wang X, Lai S, He S, Liu K, Zhao J, Lv X. Ontology matching with word embeddings. In: Sun M, Liu Z, Zhang M, Liun Y, editors. Chinese computational linguistics and natural language processing based on naturally annotated big data. Berlin: Springer; 2014. p. 34\u201345."},{"key":"1324_CR12","doi-asserted-by":"crossref","unstructured":"Kolyvakis P, Kalousis A, Kiritsis D. Deepalignment: unsupervised ontology matching with refined word vectors. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, Volume 1 (Long Papers), vol. 1. ACL, pp. 787\u2013798; 2018.","DOI":"10.18653\/v1\/N18-1072"},{"key":"1324_CR13","doi-asserted-by":"crossref","unstructured":"Lei L, Zhou Y, Zhai J, Zhang L, Fang Z, He P, Gao J. An effective patient representation learning for time-series prediction tasks based on EHRS. In: IEEE international conference on bioinformatics and biomedicine, BIBM 2018, Madrid, Spain, December 3\u20136, 2018, pp 885\u2013892; 2018.","DOI":"10.1109\/BIBM.2018.8621542"},{"issue":"1","key":"1324_CR14","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13326-018-0187-8","volume":"9","author":"P Kolyvakis","year":"2018","unstructured":"Kolyvakis P, Kalousis A, Smith B, Kiritsis D. Biomedical ontology alignment: an approach based on representation learning. J Biomed Semant. 2018;9(1):21.","journal-title":"J Biomed Semant"},{"key":"1324_CR15","doi-asserted-by":"crossref","unstructured":"Sun Z, Hu W, Zhang Q, Qu Y. Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI), pp 4396\u20134402. IJCAI; 2018","DOI":"10.24963\/ijcai.2018\/611"},{"key":"1324_CR16","first-page":"297","volume":"33","author":"BD Trisedya","year":"2019","unstructured":"Trisedya BD, Qi J, Zhang R. Entity alignment between knowledge graphs using attribute embeddings. Proc AAAI Conf Artif Intell. 2019;33:297\u2013304.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1324_CR17","unstructured":"Cucerzan S. Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). ACL, pp 708\u2013716; 2007"},{"key":"1324_CR18","unstructured":"Han X, Zhao J. Nlpr\\_kbp in tac 2009 kbp track: a two-stage method to entity linking. In: TAC. Citeseer; 2009."},{"key":"1324_CR19","doi-asserted-by":"crossref","unstructured":"Han X, Sun L, Zhao J. Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 765\u2013774; 2011.","DOI":"10.1145\/2009916.2010019"},{"key":"1324_CR20","unstructured":"Varma V, Pingali P, Katragadda R, Krishna S, Ganesh S, Sarvabhotla K, Garapati H, Gopisetty H, Reddy VB, Reddy K et\u00a0al. Iiit hyderabad at tac 2009. In: TAC; 2009."},{"key":"1324_CR21","unstructured":"Lehmann J, Monahan S, Nezda L, Jung A, Shi Y. LCCc approaches to knowledge base population at TAC 2010. In: TAC; 2010."},{"key":"1324_CR22","doi-asserted-by":"crossref","unstructured":"Moreno JG, Besan\u00e7on R, Beaumont R, D\u2019hondt E, Ligozat A-L, Rosset S, Tannier X, Grau B. Combining word and entity embeddings for entity linking. In: European semantic web conference. Springer, pp 337\u2013352; 2017.","DOI":"10.1007\/978-3-319-58068-5_21"},{"key":"1324_CR23","doi-asserted-by":"crossref","unstructured":"Shen W, Wang J, Luo P, Wang M. LINDEN: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st international conference on world wide web. ACM, pp 449\u2013458; 2012.","DOI":"10.1145\/2187836.2187898"},{"key":"1324_CR24","doi-asserted-by":"publisher","unstructured":"Zhang J, Wang Q, Zhang Z, Zhou Y, Ye Q, Zhang H, Qiu J, He P. An effective standardization method for the lab indicators in regional medical health platform using n-grams and stacking; 2019. https:\/\/doi.org\/10.1109\/BIBM.2018.8621274.","DOI":"10.1109\/BIBM.2018.8621274"},{"key":"1324_CR25","unstructured":"Settles B. Active learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences; 2009."},{"issue":"2","key":"1324_CR26","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10115-012-0507-8","volume":"35","author":"Y Fu","year":"2013","unstructured":"Fu Y, Zhu X, Li B. A survey on instance selection for active learning. Knowl Inf Syst. 2013;35(2):249\u201383.","journal-title":"Knowl Inf Syst"},{"key":"1324_CR27","doi-asserted-by":"crossref","unstructured":"Shen Y, Yun H, Lipton ZC, Kronrod Y, Anandkumar A. Deep active learning for named entity recognition; 2017. arXiv:1707.05928.","DOI":"10.18653\/v1\/W17-2630"},{"key":"1324_CR28","doi-asserted-by":"crossref","unstructured":"Joshi AJ, Porikli F, Papanikolopoulos N (2009) Multi-class active learning for image classification. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 2372\u20132379.","DOI":"10.1109\/CVPR.2009.5206627"},{"key":"1324_CR29","doi-asserted-by":"crossref","unstructured":"Hakkani-T\u00fcr D, Riccardi G, Gorin A. Active learning for automatic speech recognition. In: 2002 IEEE international conference on acoustics, speech, and signal processing, vol. 4. IEEE, p 3904; 2002.","DOI":"10.1109\/ICASSP.2002.5745510"},{"key":"1324_CR30","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding; 2018. arXiv:1810.04805."},{"key":"1324_CR31","doi-asserted-by":"crossref","unstructured":"Chen Q, Zhu X, Ling Z, Wei S, Jiang H, Inkpen D. Enhanced lstm for natural language inference; 2016. arXiv:1609.06038.","DOI":"10.18653\/v1\/P17-1152"},{"key":"1324_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103133","author":"Q Wang","year":"2019","unstructured":"Wang Q, Zhou Y, Ruan T, Gao D, Xia Y, He P. Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition. J Biomed Inform. 2019;. https:\/\/doi.org\/10.1016\/j.jbi.2019.103133.","journal-title":"J Biomed Inform"},{"issue":"3","key":"1324_CR33","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TNB.2019.2908678","volume":"18","author":"J Qiu","year":"2019","unstructured":"Qiu J, Zhou Y, Wang Q, Ruan T, Gao J. Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field. IEEE Trans NanoBiosci. 2019;18(3):306\u201315. https:\/\/doi.org\/10.1109\/TNB.2019.2908678.","journal-title":"IEEE Trans NanoBiosci"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01324-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-020-01324-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01324-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T17:10:03Z","timestamp":1608052203000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01324-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":33,"journal-issue":{"issue":"S14","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1324"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01324-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"15 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The data we used in this paper is a sub-dataset collected from SHDC, which contains a set of lab indicator terminologies. No human data, human tissue or any clinical data were used for this study. Therefore, we think approval was not need for the use of data.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"331"}}