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Comput. Healthcare"],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n            Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of\n            <jats:italic>medical-order sequence variants<\/jats:italic>\n            , where a \u201cfrequent pattern\u201d can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system\u2019s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.\n          <\/jats:p>","DOI":"10.1145\/3561825","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T12:44:29Z","timestamp":1662986669000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3702-8974","authenticated-orcid":false,"given":"Hieu Hanh","family":"Le","sequence":"first","affiliation":[{"name":"Tokyo Institute of Technology, Meguro, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5677-3112","authenticated-orcid":false,"given":"Tatsuhiro","family":"Yamada","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Meguro, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3804-4095","authenticated-orcid":false,"given":"Yuichi","family":"Honda","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Meguro, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4075-3800","authenticated-orcid":false,"given":"Takatoshi","family":"Sakamoto","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Meguro, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8432-7796","authenticated-orcid":false,"given":"Ryosuke","family":"Matsuo","sequence":"additional","affiliation":[{"name":"Life Data Initiative, Kyoto, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9297-1554","authenticated-orcid":false,"given":"Tomoyoshi","family":"Yamazaki","sequence":"additional","affiliation":[{"name":"Tokyo Insitute of Technology, Miyazaki, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0559-259X","authenticated-orcid":false,"given":"Kenji","family":"Araki","sequence":"additional","affiliation":[{"name":"University of Miyazaki Hospital"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9788-0443","authenticated-orcid":false,"given":"Haruo","family":"Yokota","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Meguro, Tokyo, Japan"}]}],"member":"320","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/978-3-642-37453-1_5","volume-title":"Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Antonio Gomariz","year":"2013","unstructured":"Gomariz Antonio, Campos Manuel, Marin Roque, and Goethals Bart. 2013. 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Retrieved from https:\/\/arxiv.org\/abs\/1712.09923.","journal-title":"arXiv:1712.09923"},{"issue":"4","key":"e_1_3_1_26_2","first-page":"e1312","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger Andreas","year":"2019","unstructured":"Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo M\u00fcller. 2019. Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9, 4 (2019), e1312.","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"10","key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MC.2021.3092610","article-title":"Toward human\u2013AI interfaces to support explainability and causability in medical AI","volume":"54","author":"Holzinger Andreas","year":"2021","unstructured":"Andreas Holzinger and Heimo M\u00fcller. 2021. 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Retrieved from https:\/\/arxiv.org\/abs\/2202.13202.","journal-title":"arXiv:2202.13202"},{"issue":"1","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.artmed.2012.06.002","article-title":"On mining clinical pathway patterns from medical behaviors","volume":"56","author":"Huang Zhengxing","year":"2012","unstructured":"Zhengxing Huang, Xudong Lu, and Huilong Duan. 2012. On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in Medicine 56, 1 (2012), 35\u201350.","journal-title":"Artificial Intelligence in Medicine"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/ICDE.2001.914830","volume-title":"Proceedings of the 17th International Conference on Data Engineering","author":"Jiawei Han","year":"2001","unstructured":"Han Jiawei, Pei Jian, Mortazavi-Asl Behzad, Pinto Helen, Chen Qiming, Dayal Umeshwar, and Hsu Mei-Chun. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the 17th International Conference on Data Engineering. 215\u2013224."},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"9256","DOI":"10.1109\/ACCESS.2017.2789324","article-title":"Predicting the risk of heart failure with EHR sequential data modeling","volume":"6","author":"Jin Bo","year":"2018","unstructured":"Bo Jin, Chao Che, Zhen Liu, Shulong Zhang, Xiaomeng Yin, and Xiaopeng Wei. 2018. Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access 6 (2018), 9256\u20139261.","journal-title":"IEEE Access"},{"key":"e_1_3_1_33_2","first-page":"677","volume-title":"Proceedings of the AMIA Annual Symposium","volume":"2015","author":"Kale David C.","year":"2015","unstructured":"David C. Kale, Zhengping Che, Mohammad Taha Bahadori, Wenzhe Li, Yan Liu, and Randall Wetzel. 2015. Causal phenotype discovery via deep networks. In Proceedings of the AMIA Annual Symposium, Vol. 2015. American Medical Informatics Association, 677\u2013686."},{"issue":"223","key":"e_1_3_1_34_2","first-page":"110","article-title":"Comparing the trends of electronic health record adoption among hospitals of the united states and japan","volume":"43","author":"Kanakubo Takako","year":"2019","unstructured":"Takako Kanakubo and Hadi Kharrazi. 2019. Comparing the trends of electronic health record adoption among hospitals of the united states and japan. Journal of Medical Systems 43, 223 (2019), 110\u2013122.","journal-title":"Journal of Medical Systems"},{"key":"e_1_3_1_35_2","first-page":"311","volume-title":"Proceedings of the 3rd SIAM International Conference on Data Mining","author":"Kum Hye-Chung","year":"2003","unstructured":"Hye-Chung Kum, Jian Pei, Wei Wang, and Dean Duncan. 2003. ApproxMAP: Approximate mining of consensus sequential patterns. In Proceedings of the 3rd SIAM International Conference on Data Mining. SIAM, 311\u2013315."},{"issue":"1","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/sam.11192","article-title":"Mining compressing sequential patterns","volume":"7","author":"Lam Hoang Thanh","year":"2014","unstructured":"Hoang Thanh Lam, Fabian M\u00f6rchen, Dmitriy Fradkin, and Toon Calders. 2014. Mining compressing sequential patterns. Statistical Analysis and Data Mining: The ASA Data Science Journal 7, 1 (2014), 34\u201352.","journal-title":"Statistical Analysis and Data Mining: The ASA Data Science Journal"},{"key":"e_1_3_1_37_2","first-page":"1726","volume-title":"Proceedings of the 4th International Conference on Computer Science and Computational Intelligent","author":"Le Hieu Hanh","year":"2017","unstructured":"Hieu Hanh Le, Henrik Edman, Yuichi Honda, Muneo Kushima, Tomoyoshi Yamazaki, Kenji Araki, and Haruo Yokota. 2017. Fast generation of clinical pathways including time intervals in sequential pattern mining on electronic medical record systems. In Proceedings of the 4th International Conference on Computer Science and Computational Intelligent. 1726\u20131731."},{"key":"e_1_3_1_38_2","first-page":"56","volume-title":"Proceedings of the 34th IEEE Symposium on Computer-based Medical Systems","author":"Le Hieu Hanh","year":"2021","unstructured":"Hieu Hanh Le, Yutaka Horino, Tomoyoshi Yamazaki, Kenji Araki, and Haruo Yokota. 2021. Sequential pattern mining of large combinable items with values for a set-of-items recommendation. In Proceedings of the 34th IEEE Symposium on Computer-based Medical Systems. IEEE, 56\u201361."},{"key":"e_1_3_1_39_2","first-page":"393","volume-title":"Proceedings of the 30th International Conference on Database and Expert Systems Applications","author":"Le Hieu Hanh","year":"2019","unstructured":"Hieu Hanh Le, Tatsuhiro Yamada, Yuichi Honda, Masaaki Kayahara, Muneo Kushima, Kenji Araki, and Haruo Yokota. 2019. Analyzing sequence pattern variants in sequential pattern mining and its application to electronic medical record systems. In Proceedings of the 30th International Conference on Database and Expert Systems Applications. Springer, 393\u2013408."},{"key":"e_1_3_1_40_2","first-page":"716","volume-title":"Proceedings of the 4th International Conference on Network and Parallel Computing Workshops","author":"Li Chaofeng","year":"2007","unstructured":"Chaofeng Li and Yansheng Lu. 2007. Similarity measurement of web sessions by sequence alignment. In Proceedings of the 4th International Conference on Network and Parallel Computing Workshops. IEEE, 716\u2013720."},{"issue":"3","key":"e_1_3_1_41_2","first-page":"1","article-title":"Scalable mining of high-utility sequential patterns with three-tier mapreduce model","volume":"16","author":"Lin Jerry Chun-Wei","year":"2021","unstructured":"Jerry Chun-Wei Lin, Youcef Djenouri, Gautam Srivastava, Yuanfa Li, and Philip S. Yu. 2021. Scalable mining of high-utility sequential patterns with three-tier mapreduce model. ACM Transactions on Knowledge Discovery from Data 16, 3 (2021), 1\u201326.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_42_2","article-title":"Phenotyping of clinical time series with LSTM recurrent neural networks","author":"Lipton Zachary C.","year":"2015","unstructured":"Zachary C. Lipton, David C. Kale, and Randall C. Wetzel. 2015. Phenotyping of clinical time series with LSTM recurrent neural networks. arXiv:1510.07641. Retrieved from https:\/\/arxiv.org\/abs\/1510.07641.","journal-title":"arXiv:1510.07641"},{"key":"e_1_3_1_43_2","first-page":"755","volume-title":"Proceedings of the 4th International Conference on Web Intelligence","author":"Lo Shuchuan","year":"2005","unstructured":"Shuchuan Lo. 2005. Binary prediction based on weighted sequential mining method. In Proceedings of the 4th International Conference on Web Intelligence. IEEE, 755\u2013761."},{"issue":"4","key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.5455\/aim.2008.16.219-225","article-title":"Evidence based medicine\u2013new approaches and challenges","volume":"16","author":"Masic Izet","year":"2008","unstructured":"Izet Masic, Milan Miokovic, and Belma Muhamedagic. 2008. Evidence based medicine\u2013new approaches and challenges. 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Retrieved September, 2021 from http:\/\/www.iryohoken.go.jp\/shinryohoshu\/kaitei\/."},{"key":"e_1_3_1_47_2","volume-title":"International Classification of Diseases","author":"Organization World Health","year":"2019","unstructured":"World Health Organization. 2019. International Classification of Diseases. Retrieved September, 2021 from https:\/\/www.who.int\/standards\/classifications\/classification-of-diseases."},{"issue":"5","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1007\/s10618-016-0467-9","article-title":"Skopus: Mining top-k sequential patterns under leverage","volume":"30","author":"Petitjean Fran\u00e7ois","year":"2016","unstructured":"Fran\u00e7ois Petitjean, Tao Li, Nikolaj Tatti, and Geoffrey I. Webb. 2016. Skopus: Mining top-k sequential patterns under leverage. 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In Proceedings the 10th International Conference on Information and Knowledge Management. 81\u201388."},{"key":"e_1_3_1_51_2","first-page":"3274","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence","author":"Prakash Aaditya","year":"2017","unstructured":"Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek Datla, Kathy Lee, Ashequl Qadir, Joey Liu, and Oladimeji Farri. 2017. Condensed memory networks for clinical diagnostic inferencing. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 3274\u20133280."},{"issue":"1","key":"e_1_3_1_52_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5121\/ijdms.2015.7103","article-title":"Mining closed sequential patterns in large sequence databases","volume":"7","author":"Purushothama Raju V.","year":"2015","unstructured":"Raju V. Purushothama and Varma G. P. Saradhi. 2015. Mining closed sequential patterns in large sequence databases. 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In Proceedings of the 11th International Conference on Data Engineering. IEEE, 3\u201314."},{"issue":"23","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"9236","DOI":"10.1016\/j.eswa.2015.07.040","article-title":"Declarative process mining in healthcare","volume":"42","author":"Rovani Marcella","year":"2015","unstructured":"Marcella Rovani, Fabrizio M. Maggi, Massimiliano De Leoni, and Wil M. P. Van Der Aalst. 2015. Declarative process mining in healthcare. Expert Systems with Applications 42, 23 (2015), 9236\u20139251.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_1_56_2","first-page":"3","volume-title":"Proceedings of the Seminars in Perinatology","volume":"21","author":"Sackett David L.","year":"1997","unstructured":"David L. Sackett. 1997. Evidence-based medicine. In Proceedings of the Seminars in Perinatology, Vol. 21. Elsevier, 3\u20135."},{"key":"e_1_3_1_57_2","first-page":"095","volume-title":"Proceedings of the 7th Australasian Data Mining Conference","author":"Saneifar Hassan","year":"2008","unstructured":"Hassan Saneifar, Sandra Bringay, Anne Laurent, and Maguelonne Teisseire. 2008. S2MP: Similarity measure for sequential patterns. In Proceedings of the 7th Australasian Data Mining Conference. ACS, 095\u2013104."},{"issue":"16","key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"12669","DOI":"10.1109\/JIOT.2020.3026826","article-title":"Large-scale high-utility sequential pattern analytics in internet of things","volume":"8","author":"Srivastava Gautam","year":"2020","unstructured":"Gautam Srivastava, Jerry Chun-Wei Lin, Xuyun Zhang, and Yuanfa Li. 2020. Large-scale high-utility sequential pattern analytics in internet of things. 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Retrieved September 2021 from http:\/\/www.corecreate.com\/02_01_izanami.html."},{"issue":"11","key":"e_1_3_1_61_2","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): Toward medical XAI","volume":"32","author":"Tjoa Erico","year":"2020","unstructured":"Erico Tjoa and Cuntai Guan. 2020. A survey on explainable artificial intelligence (XAI): Toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems 32, 11 (2020), 4793\u20134813.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1016\/j.eswa.2011.09.049","article-title":"Generating touring path suggestions using time-interval sequential pattern mining","volume":"39","author":"Tsai Chieh-Yuan","year":"2012","unstructured":"Chieh-Yuan Tsai, James J. H. Liou, Chih-Jung Chen, and Ching-Chuan Hsiao. 2012. Generating touring path suggestions using time-interval sequential pattern mining. Expert Systems with Applications 39, 3 (2012), 3593\u20133602.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_1_63_2","first-page":"1","article-title":"Mining clinical pathways using dual clustering","author":"Tsumoto Shusaku","year":"2021","unstructured":"Shusaku Tsumoto, Shoji Hirano, and Tomohiro Kimura. 2021. Mining clinical pathways using dual clustering. The Review of Socionetwork Strategies (2021), 1\u201321.","journal-title":"The Review of Socionetwork Strategies"},{"issue":"4","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1007\/s10115-004-0175-4","article-title":"TSP: Mining top-k closed sequential patterns","volume":"7","author":"Tzvetkov Petre","year":"2005","unstructured":"Petre Tzvetkov, Xifeng Yan, and Jiawei Han. 2005. TSP: Mining top-k closed sequential patterns. Knowledge and Information Systems 7, 4 (2005), 438\u2013457.","journal-title":"Knowledge and Information Systems"},{"key":"e_1_3_1_65_2","volume-title":"Clinical Research that Must be Published (Public Disclosure) (in Japanese)","author":"Hospital Clinical Research Support Center University of Miyazaki","year":"2016","unstructured":"Clinical Research Support Center University of Miyazaki Hospital. 2016. Clinical Research that Must be Published (Public Disclosure) (in Japanese). Retrieved August 2022 from http:\/\/www.med.miyazaki-u.ac.jp\/home\/crsc\/patient\/notice\/."},{"key":"e_1_3_1_66_2","first-page":"20","volume-title":"Proceedings of the 2016 IEEE Symposium on Computers and Communication","author":"Uragaki Keishiro","year":"2016","unstructured":"Keishiro Uragaki, Tomoyuki Hosaka, Yoshitaka Arahori, Muneo Kushima, Tomoyoshi Yamazaki, Kenji Araki, and Haruo Yokota. 2016. Sequential pattern mining on electronic medical records with handling time intervals and the efficacy of medicines. In Proceedings of the 2016 IEEE Symposium on Computers and Communication. IEEE, 20\u201325."},{"issue":"8","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ijmedinf.2009.03.003","article-title":"What are the standard functions of electronic clinical pathways?","volume":"78","author":"Wakamiya Shunji","year":"2009","unstructured":"Shunji Wakamiya and Kazunobu Yamauchi. 2009. What are the standard functions of electronic clinical pathways? International Journal of Medical Informatics 78, 8 (2009), 543\u2013550.","journal-title":"International Journal of Medical Informatics"},{"issue":"8","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TKDE.2007.1043","article-title":"Frequent closed sequence mining without candidate maintenance","volume":"19","author":"Wang Jianyong","year":"2007","unstructured":"Jianyong Wang, Jiawei Han, and Chun Li. 2007. Frequent closed sequence mining without candidate maintenance. 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NetNCSP: Nonoverlapping closed sequential pattern mining. Knowledge-based Systems 196 (2020), 105812.","journal-title":"Knowledge-based Systems"},{"key":"e_1_3_1_73_2","first-page":"166","volume-title":"Proceedings of the International Conference on Data Mining","author":"Xifeng Yan","year":"2003","unstructured":"Yan Xifeng, Han Jiawei, and Afshar Ramin. 2003. CloSpan: Mining closed sequential patterns in large datasets. In Proceedings of the International Conference on Data Mining. SIAM, 166\u2013177."},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","article-title":"Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond","volume":"77","author":"Yang Guang","year":"2022","unstructured":"Guang Yang, Qinghao Ye, and Jun Xia. 2022. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion 77 (2022), 29\u201352.","journal-title":"Information Fusion"},{"key":"e_1_3_1_75_2","first-page":"48","volume-title":"Proceedings of the 17th Database Systems for Advanced Applications","author":"Yap Ghim-Eng","year":"2012","unstructured":"Ghim-Eng Yap, Xiao-Li Li, and Philip S. Yu. 2012. Effective next-items recommendation via personalized sequential pattern mining. In Proceedings of the 17th Database Systems for Advanced Applications. 48\u201364."},{"issue":"3","key":"e_1_3_1_76_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.37737\/ace.1.3_76","article-title":"Real world data in japan: Chapter II the diagnosis procedure combination database","volume":"1","author":"Yasunaga Hideo","year":"2019","unstructured":"Hideo Yasunaga. 2019. Real world data in japan: Chapter II the diagnosis procedure combination database. 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