{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T12:12:44Z","timestamp":1782821564601,"version":"3.54.5"},"reference-count":56,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T00:00:00Z","timestamp":1457913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>\u2013 Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>\u2013 It is a combined DM (clustering and the nearest neighbor) technique for outliers\u2019 detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients\u2019 events and situations prove that play a significant role in the process of patient care safety and medications.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers\u2019 satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>\u2013 This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-07-2015-0024","type":"journal-article","created":{"date-parts":[[2016,3,9]],"date-time":"2016-03-09T10:33:21Z","timestamp":1457519601000},"page":"42-68","source":"Crossref","is-referenced-by-count":33,"title":["Combined data mining techniques based patient data outlier detection for healthcare safety"],"prefix":"10.1108","volume":"9","author":[{"given":"Gebeyehu Belay","family":"Gebremeskel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chai","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongshi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawit","family":"Haile","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2020121704494087200_b1","unstructured":"Anbarasi, M.S. , Ghaayathri, S. , Kamaleswari, R. and Abirami, I. (2011), \u201cOutlier detection for multidimensional medical data\u201d, International Journal of Computer Science and Information Technologies , Vol. 2 No. 1, pp. 512-516."},{"key":"key2020121704494087200_b2","unstructured":"Andrew, M. , Nigam, K. and Ungar, L.H. (2000), Efficient Clustering of High-Dimensional Datasets with Application to Reference Matching , ACM, Boston, MA, pp. 169-178. doi: 1-58113-233-6\/00\/08."},{"key":"key2020121704494087200_b16","doi-asserted-by":"crossref","unstructured":"Angiulli, F. , Basta, S. and Pizzuti, C. (2006), \u201cDistance based detection and prediction of outliers\u201d, IEEE, Transactions on Knowledge and Data Engineering , Vol. 18 No. 2, pp. 145-160.","DOI":"10.1109\/TKDE.2006.29"},{"key":"key2020121704494087200_b4","unstructured":"Apurva, M. and Honeywell , ACS-Labs (2014), \u201cA medical domain collaborative anomaly detection framework for identifying medical identity theft\u201d, IEEE, Collaboration Technologies and Systems International Conference, pp. 428-435. doi: z10.1109."},{"key":"key2020121704494087200_b40","doi-asserted-by":"crossref","unstructured":"Barlas, P. , Lanning, I. and Heavey, C. (2015), \u201cA survey of open source data science tools\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 8 No. 3, pp. 232-261.","DOI":"10.1108\/IJICC-07-2014-0031"},{"key":"key2020121704494087200_b49","doi-asserted-by":"crossref","unstructured":"B\u00f6hme, T. , Williams, S. , Childerhouse, P. , Deakins, E. and Towill, D. (2014), \u201cSquaring the circle of healthcare supplies\u201d, Journal of Health Organization and Management , Vol. 28 No. 2, pp. 247-265.","DOI":"10.1108\/JHOM-01-2013-0014"},{"key":"key2020121704494087200_b5","unstructured":"Behera, S. et al. (2012), \u201cNew hybridized k-means clustering based outlier detection technique for effective data mining\u201d, IJARCaSE , Vol. 2 No. 4, pp. 287-292."},{"key":"key2020121704494087200_b33","doi-asserted-by":"crossref","unstructured":"Breunig, M.M. , Kriegel, H.-P. , Ng, R.T. and Sander, J. (2000), \u201cLOF: identifying density-based local outliers\u201d, Proceedings of the ACM SIGMOD International Conference on Management of Data, Dalles, TX, pp. 1-12.","DOI":"10.1145\/342009.335388"},{"key":"key2020121704494087200_b7","unstructured":"Carolyn, J.H. (2012), \u201cThe role of evidence-based clinical practice in emerging care models of home care\u201d, McKesson Corporation, Extended Care Solutions Group , pp. 1-9."},{"key":"key2020121704494087200_b32","unstructured":"Charles, M.-J. , Harmon, B.J. and Jordan, P.S. (2005), \u201cImproving patient safety with the military electronic health record\u201d, Advances in Patient Safety , Vol. 3, pp. 23-34, Agency for Healthcare Research and Quality (US)."},{"key":"key2020121704494087200_b26","doi-asserted-by":"crossref","unstructured":"Cios, K.J. and Moore, G.W. (2002), \u201cUniqueness of medical data mining\u201d, Artificial Intelligence in Medicine , Vol. 26, pp. 1-24.","DOI":"10.1016\/S0933-3657(02)00049-0"},{"key":"key2020121704494087200_b10","doi-asserted-by":"crossref","unstructured":"Daniel, F. and Luca, L. (2013), \u201cA procedure for analyses the strategic outliers and the multiple motivations in a contingent valuation\u201d, International Journal of Social Economics , Vol. 40 No. 3, pp. 246-26.","DOI":"10.1108\/03068291311291527"},{"key":"key2020121704494087200_b11","doi-asserted-by":"crossref","unstructured":"Debbie, T. (2008), \u201cThe emerging field of informatics\u201d, North Carolina Medical Journal , Vol. 69 No. 2, pp. 127-131.","DOI":"10.18043\/ncm.69.2.127"},{"key":"key2020121704494087200_b12","doi-asserted-by":"crossref","unstructured":"Deshpande, S.P. and Thakare, V.M. (2010), \u201cData mining system and applications: a review\u201d, International Journal of Distributed and Parallel Systems (IJDPS) , Vol. 1 No. 1, pp. 32-44.","DOI":"10.5121\/ijdps.2010.1103"},{"key":"key2020121704494087200_b14","unstructured":"Durairaj, M. and Ranjani, V. (2013), \u201cData mining applications in healthcare sector: a study\u201d, International Journal of Scientific and Technology Research , Vol. 2 No. 10, pp. 29-35."},{"key":"key2020121704494087200_b36","doi-asserted-by":"crossref","unstructured":"Elg, M. , Palmberg, K. and Kollberg, B.B. (2013), \u201cPerformance measurement to drive improvements in healthcare practice\u201d, International Journal of Operations & Production Management , Vol. 33 Nos 11\/12, pp. 1623-1651.","DOI":"10.1108\/IJOPM-07-2010-0208"},{"key":"key2020121704494087200_b8","doi-asserted-by":"crossref","unstructured":"Fan, C.Y. , Chan, P.-C. , Lin, J.-J. and Hsieh, J.C. (2011), \u201cA hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification\u201d, Applied Soft Computing , Vol. 11, pp. 632-644.","DOI":"10.1016\/j.asoc.2009.12.023"},{"key":"key2020121704494087200_b17","doi-asserted-by":"crossref","unstructured":"Gebremeskel, G.B. , Yi, C. , Wang, C. and He, Z. (2015), \u201cCritical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques\u201d, Industrial Management & Data Systems , Vol. 115 No. 6, pp. 1151-1178.","DOI":"10.1108\/IMDS-12-2014-0386"},{"key":"key2020121704494087200_b43","unstructured":"Guha, R. , Dutta, D. , Jurs, P.C. and Chen, T. (2006), R-NN Curves: An Intuitive Approach to Outlier Detection Using a Distance Based Method , American Chemical Society J. Chem. Inf, Model, CA. 46, pp. 1713-1722. doi: 10.1021\/ci060013."},{"key":"key2020121704494087200_b41","unstructured":"Hammer, P.L. and Bonates, T.O. (2006), \u201cLogical analysis of data: an overview: from combinatorial optimization to medical applications\u201d, Springer, LLC, pp. 203-225. doi: 10.1007\/s10479-006-0075."},{"key":"key2020121704494087200_b19","unstructured":"Hian, C.K. and Gerald, T. (2005), \u201cData mining applications in healthcare\u201d, Journal of Healthcare Information Management , Vol. 19 No. 2, pp. 64-72."},{"key":"key2020121704494087200_b20","unstructured":"Irad, B.-G. (2005), Outlier Detection, Data Mining, and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers , Kluwer Academic Publishers, ISBN 0-387-24435-2."},{"key":"key2020121704494087200_b21","unstructured":"James, B. and David, J.H. (2012), \u201cData mining from a patient safety database: the lessons learned\u201d, Data Mining and Knowledge Discovery , Vol. 24, pp. 195-217. doi: 10.1007\/s10618-011-0225."},{"key":"key2020121704494087200_b22","doi-asserted-by":"crossref","unstructured":"Jan, P. (2000), \u201cA theory of proximity-based clustering: structure detection by optimization\u201d, Pergamon, The Journal of Pattern Recognition Society , Vol. 33, pp. 617-634.","DOI":"10.1016\/S0031-3203(99)00076-X"},{"key":"key2020121704494087200_b24","unstructured":"Karanjit, S. and Shuchita, U. (2012), \u201cOutlier detection: applications and techniques\u201d, IJCSI International Journal of Computer Science Issues , Vol. 9 Nos 1-3, pp. 307-323."},{"key":"key2020121704494087200_b15","doi-asserted-by":"crossref","unstructured":"Keogh, E. , Lin, J. , Fu, A.W. and Van Herle, H. (2006), \u201cFinding unusual medical time-series subsequences: algorithms and applications\u201d, IEEE Transactions on Information Technology in Biomedicine , Vol. 10 No. 3, pp. 429-439.","DOI":"10.1109\/TITB.2005.863870"},{"key":"key2020121704494087200_b47","doi-asserted-by":"crossref","unstructured":"Kim, S. , Cho, N.W. , Kang, B. and Ka, S.-H. (2011), \u201cFast outlier detection for very large log data\u201d, Elsevier, Expert Systems with Application , Vol. 38, pp. 9587-9596.","DOI":"10.1016\/j.eswa.2011.01.162"},{"key":"key2020121704494087200_b25","doi-asserted-by":"crossref","unstructured":"Kotagiri, R. and Hongjian, F. (2007), \u201cPatterns based classi\ufb01ers\u201d, Springer Science Business Media, LLC, World Wide Web10. doi: 10.1007\/s11280-006-0012-7.","DOI":"10.1007\/s11280-006-0012-7"},{"key":"key2020121704494087200_b27","doi-asserted-by":"crossref","unstructured":"Kumar, S.R. (2012), \u201cPattern classification using fuzzy relation and genetic algorithm\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 5 No. 4, pp. 533-565.","DOI":"10.1108\/17563781211282277"},{"key":"key2020121704494087200_b51","unstructured":"Kumar, V. , Kumar, D. and Singh, R.K. (2008), \u201cOutlier mining in medical databases: an application of data mining in healthcare management to detect abnormal values presented in medical databases\u201d, IJCSNS , Vol. 8 No. 8, pp. 272-277."},{"key":"key2020121704494087200_b28","doi-asserted-by":"crossref","unstructured":"Lu, L. , Zhang, H. and Gao, X.-Z. (2015), \u201cIntegrate inconsistent and heterogeneous data based on user feedback\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 8 No. 2, pp. 187-203.","DOI":"10.1108\/IJICC-04-2014-0013"},{"key":"key2020121704494087200_b29","doi-asserted-by":"crossref","unstructured":"Mahasak, K. and Thittaporn, G. (2015), \u201cThe analysis of lane detection algorithms using histogram shapes and Hough transform\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 8 No. 3, pp. 262-278.","DOI":"10.1108\/IJICC-05-2014-0024"},{"key":"key2020121704494087200_b30","unstructured":"Manzoor, E. (2009), Ef\ufb01cient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream , IEEE, Shandong, pp. 298-304. doi: 10.1109\/FSKD.374."},{"key":"key2020121704494087200_b31","doi-asserted-by":"crossref","unstructured":"Mao, Y. , Chen, Y. , Hackmann, G. , Chen, M. , Lu, C. , Kollef, M. and Bailey, T.C. , (2011), \u201cMedical data mining for early deterioration warning in general hospital wards\u201d, IEEE Proceedings, 11th International Conference on Data Mining, pp. 1042-1049.","DOI":"10.1109\/ICDMW.2011.117"},{"key":"key2020121704494087200_b34","unstructured":"Mary, K.O. (2004), \u201cApplication of data mining techniques to healthcare data\u201d, Vol. 25 No. 8, Statistics for Hospital Epidemiology, pp. 690-695."},{"key":"key2020121704494087200_b35","doi-asserted-by":"crossref","unstructured":"Mathew, E.O. and Amol, G. (2006), Fast Distributed Outlier Detection in Mixed-Attribute Data-Sets , Springer, Data Mining and Knowledge Discovery, Ohio, OH, 12, pp. 203-228. doi: 10.1007\/s10618-005-0014-6.","DOI":"10.1007\/s10618-005-0014-6"},{"key":"key2020121704494087200_b3","doi-asserted-by":"crossref","unstructured":"Mitchell, A.F.S. and Krzanowski, W.J. (1985), \u201cThe mahalanobis distance and elliptic distributions\u201d, Biometrika , Vol. 72 No. 2, pp. 464-467.","DOI":"10.1093\/biomet\/72.2.464"},{"key":"key2020121704494087200_b38","unstructured":"Nada, L. (1999), Selected techniques for data mining in medicine\u201d, Elsevier , Arti\ufb01cial Intelligence in Medicine, Vol. 16 pp. 3-23."},{"key":"key2020121704494087200_b18","doi-asserted-by":"crossref","unstructured":"Nemati, H.R. and Barko, C.D. (2003), \u201cKey factors for achieving organizational data-mining success\u201d, Industrial Management & Data Systems , Vol. 103 No. 4, pp. 282-292.","DOI":"10.1108\/02635570310470692"},{"key":"key2020121704494087200_b39","unstructured":"Nguyen, T.-D. and Welsch, R.E. (2010), \u201cOutlier detection and robust covariance estimation using mathematical programming\u201d, Advances in Data Analysis and Classification , Vol. 4, pp. 301-334. doi: 10-1007\/s11634-010-0070-7."},{"key":"key2020121704494087200_b23","doi-asserted-by":"crossref","unstructured":"Oh, J.H. , Gao, J. and Rosenblatt, K. (2008), \u201cBiological data outlier detection based on kullback-leibler divergence\u201d, IEEE International Conference on Bioinformatics and Biomedicine, 978-0-7695-3452-7\/08, pp. 249-254.","DOI":"10.1109\/BIBM.2008.76"},{"key":"key2020121704494087200_b42","doi-asserted-by":"crossref","unstructured":"Petrovskiy, M.I. (2003), \u201cOutlier detection algorithms in data mining systems\u201d, Programming and Computer Software , Vol. 29 No. 4, pp. 228-237.","DOI":"10.1023\/A:1024974810270"},{"key":"key2020121704494087200_b6","doi-asserted-by":"crossref","unstructured":"Pontes, B. , Divina, F. , Giraldez, R. and Aguilar-Ruiz, J.S. (2009), \u201cImproved biclustering on expression data through overlapping control\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 2 No. 3, pp. 477-493.","DOI":"10.1108\/17563780910982707"},{"key":"key2020121704494087200_b37","doi-asserted-by":"crossref","unstructured":"Radovanovi, M. , Nanopoulos, A. and Ivanovi, M. (2009), \u201cNearest neighbors in high-dimensional data: the emergence and in\ufb02uence of hubs\u201d, Proceeding Appearing in the 26th International Conference on Machine Learning, pp. 1-8.","DOI":"10.1145\/1553374.1553485"},{"key":"key2020121704494087200_b44","unstructured":"Riccardo, B. and Blaz, Z. (2008), \u201cReview: predictive data mining in clinical medicine: current issues and guidelines\u201d, International Journal of Medical Informatics , Elsevier, pp. 81-97."},{"key":"key2020121704494087200_b46","doi-asserted-by":"crossref","unstructured":"Sean, N.G. and Thunshun, W.L. (2008), \u201cMedical data mining by fuzzy modeling with selected features\u201d, Artificial Intelligence in Medicine , Elsevier, Vol. 43, pp. 195-206.","DOI":"10.1016\/j.artmed.2008.04.004"},{"key":"key2020121704494087200_b48","unstructured":"Silvia, C. (2008), \u201cOutlier detection methods for industrial applications\u201d, Advances in Robotics, Automation and Control , pp. 265-282, ISBN 78-953-7619-16-9."},{"key":"key2020121704494087200_b56","unstructured":"Tang, Z. , Yang, J. and Yang, B. (2010), A New Outlier Detection Algorithm Based on Manifold Learning , IEEE, Beijing, 978-1-4244-5182-1\/10, pp. 452-457."},{"key":"key2020121704494087200_b45","doi-asserted-by":"crossref","unstructured":"Tibshirani, R. , Walther, G. and Hastie, T. (2001), \u201cEstimating the number of clusters in a data-sets via the gap statistic\u201d, Journal of the Royal Statistical Society B, Vol. 63 No. 2, pp. 211-423.","DOI":"10.1111\/1467-9868.00293"},{"key":"key2020121704494087200_b50","doi-asserted-by":"crossref","unstructured":"Todorov, V. , Templ, M. and Filzmoser, P. (2011), \u201cDetection of multivariate outliers in business survey data with incomplete information\u201d, Advances in Data Analysis and Classification , Vol. 5, pp. 37-56. doi: 10.1007\/s11634-010-0075-2.","DOI":"10.1007\/s11634-010-0075-2"},{"key":"key2020121704494087200_b13","doi-asserted-by":"crossref","unstructured":"Tomar, D. and Agarwal, S. (2013), \u201cA survey on data mining approaches for healthcare\u201d, International Journal of Bio-Science and Bio-Technology , Vol. 5 No. 5, pp. 241-266.","DOI":"10.14257\/ijbsbt.2013.5.5.25"},{"key":"key2020121704494087200_b53","unstructured":"Xiaogang, S. and Chih-Ling, T. (2011), Outlier Detection , John Wiley & Sons, Inc, Vol. 1, pp. 261-268."},{"key":"key2020121704494087200_b54","unstructured":"Yong, S. and Li, Z. (2011), \u201cCOID: A cluster\u2013outlier iterative detection approach to multi-dimensional data analysis\u201d, Knowledge and Information Systems , Vol. 27, pp. 709-733. doi: 10.1007\/s10115-010-0323."},{"key":"key2020121704494087200_b55","unstructured":"Yong, Y. and Maddala, G.S. (2015), \u201cThe effects of different types of outliers on unit root tests in messy data\u201d, Published Online , Vol. 8, pp. 269-305."},{"key":"key2020121704494087200_b52","doi-asserted-by":"crossref","unstructured":"Zhang, X. , Jing, L. , Hu, X. , Ng, M. , Xia, J. and Zhou, X. (2008), \u201cMedical document clustering using ontology-based term similarity measures\u201d, International Journal of Data Warehousing & Mining , Vol. 4 No. 1, pp. 62-73.","DOI":"10.4018\/jdwm.2008010104"},{"key":"key2020121704494087200_b9","doi-asserted-by":"crossref","unstructured":"Zhu, C. , Kitagawa, H. , Papadimitriou, S. and Faloutsos, C. (2011), \u201cOutlier detection, for example\u201d, Journal of Intelligent Information Systems , Vol. 36, pp. 217-247. doi: 10.1007\/s10844-010-0128-1.","DOI":"10.1007\/s10844-010-0128-1"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/IJICC-07-2015-0024","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-07-2015-0024\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-07-2015-0024\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:30Z","timestamp":1753397670000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/9\/1\/42-68\/134648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,14]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,3,14]]}},"alternative-id":["10.1108\/IJICC-07-2015-0024"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-07-2015-0024","relation":{},"ISSN":["1756-378X"],"issn-type":[{"value":"1756-378X","type":"print"}],"subject":[],"published":{"date-parts":[[2016,3,14]]}}}