Abstract
As the development of IT convergence technology reaches its zenith, data in almost all areas have been developed and operated as a system after digitalization. To acquire more diverse and in-depth information, humans are actively engaged in information filtering. In the medical and health industries, most medical information is organized in a system and utilized for efficient health management as well as in various areas such as U-healthcare. Due to aging and chronic disease, interest in health management has intensified. As a result, health prevention and management through U-healthcare has been developed. However, there has been no study on pain in patients suffering from chronic disease. Regarding pain-related decisions by patients, sustainable and effective management is required, unlike acute disease patients. In this paper, we proposes the decision supporting method for chronic disease patients based on mining frequent pattern tree. The proposed method is measures for pain-related decision making by chronic disease-suffering patients using a frequent pattern tree for data preprocessing, extraction, and data mining of conventional medical data. By utilizing the basic information of patients, which are the foundation for pain-related decision making, normalization can be applied to the frequent pattern tree of data mining. The pain forecast supports pain-related decision making by extracting similar patients’ information in a frequent pattern tree based on Electronic Medical Records (EMR).
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Wonju Severance Christian Hospital, http://www.wch.or.kr/
References
Adam W, Sitting FD (2008) A four-phase model of the evolution of clinical decision support architectures. J Med Inform 77(10):641–649
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proc. of ACM SIGMOD on Management of Data pp. 207–216
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proc. of the 20th International Conference on Very Large Databases pp. 487–499
Agrawal R, Srikant R (1995) Mining sequential patterns. Proc. of the Int Conference on Data Engineering. Taipei, Taiwan
Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using decision tree fuzzy rule. Int J Res Rev Comput Sci 3(3):1659–1667
Baek SJ, Han JS, Chung KY (2013) Dynamic reconfiguration based on goal-scenario by adaptation strategy. Wirel Pers Commun. doi:10.1007/s11277-013-1239-0
Brekken SA, Sheets VJD (2008) Pain management: a regulatory issue. J Nurs Adm Q 32(4):288–295
Choi SH, Jo BH, Ji HR, Lee UJ, Kim UJ, Kim HS, Park MJ (2004) Standardized of nursing diagnosis, intervention and outcome. JeongMunGak
Chung KY (2013) Effect of facial makeup style recommendation on visual sensibility. Multimed Tools Appl. doi:10.1007/s11042-013-1355-6
Chung KY, Yoo J, Kim KJ (2013) Recent trends on mobile computing and future networks. Pers Ubiquit Comput. doi:10.1007/s00779-013-0682-y
Clifton C, Marks D (1996) Security and privacy implications of data mining. In: Workshop on data mining and knowledge discovery. Montreal, Canada pp. 15–10
Cooley R, Mobasher B, Srivastava J (1999) Data preparation for mining world wide web browsing patterns. Knowl Inf Syst 1(1):5–32
Greenes RA (ed) (2007) Clinical decision support. Elsevier Science, San Diego
Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. In: Workshop on data mining and knowledge discovery, 8 pp. 53–87
Hyun Y, Jo SM, Chung KY (2011) Pain nursing intervention supporting method using collaborative filtering in health industry. J Korea Contents Assoc 11(7):1–8
International Association for the Study of Pain (1986) Pain terms a current list of the definitions and notes on usage. J Pain 3:216–221
Jang YJ (2004) Post-operative pain of spinal surgery patients and satisfaction about intervention of pain control. Chonbuk University, South Korea
Jung KY, Lee JH (2004) User preference mining through hybrid collaborative filtering and content-based filtering in recommendation system. IEICE Trans Inf Syst E87-D(12):2781–2790
Jung KI, Park JS, Kim HO, Yun MO, Mun MY (2004) A survey of nurses’ and doctors’ knowledge toward cancer pain management. J Korean Clin Nurs Res 10(1):111–124
Jung H, Hyun Y, Chung K-Y, Lee Y-H (2013) Performance analysis of intelligence pain nursing intervention U-health system. J Korea Contents Assoc 13(4):1–7
Kang SK, Chung KY, Lee JH (2013) Development of head detection and tracking systems for visual surveillance. Pers Ubiquit Comput. doi:10.1007/s00779-013-0668-9
Kim SH, Chung KY (2013) 3D simulator for stability analysis of finite slope causing plane activity. Multimed Tools Appl. doi:10.1007/s11042-013-1356-5
Kim JH, Chung KY (2013) Ontology-based healthcare context information model to implement ubiquitous environment. Multimed Tools Appl. doi:10.1007/s11042-011-0919-6
Kim SH, Chung KY (2013) Medical information service system based on human 3D anatomical model. Multimed Tools Appl. doi:10.1007/s11042-013-1584-8
Kim GH, Kim YG, Chung KY (2013) Towards virtualized and automated software performance test architecture. Multimed Tools Appl. doi:10.1007/s11042-013-1536-3
Kim JH, Lee D, Chung KY (2013) Multimedia tools and applications. Multimed Tools Appl. doi:10.1007/s11042-011-0920-0
Kim JH, Lee YH, Yang BM (2008) A national survey of postoperative pain managements in hospitals from The National Health Insurance Database. J Korean Soc Anesthesiologists 55(4):458–466
Kiyong N, Heon Gyu L, Keun Ho R (2007) Data mining approach for diagnosing heart disease. Korea Res Inst Stand Sci 10(2):147–154
Ko JW, Chung KY, Han JS (2013) Model transformation verification using similarity and graph comparison algorithm. Multimed Tools Appl. doi:10.1007/s11042-013-1581-y
Korea Centers for Disease Control and Prevention (2010) 5th Korean National Health and Nutrition Examinations Survey (KNHANES V-1). Centers for Disease Control and Prevention
Lee JK (2000) Study on internet personalized item recommendation algorithms using collaborative filtering. Master Paper, Yonsei University, pp 23–24
Lee PS, Kim SI, Kim SY, Lee SJ, Park ES, Park YJ, Rhu HS, Chang SO, Han KS, Suk MH (2002) Nursing need of patients with chronic illness. J Korea Comm Health Nurs Acad Soc 32:165–175
Lee KD, Nam MY, Chung KY, Lee YH, Kang UG (2013) Context and profile based cascade classifier for efficient people detection and safety care system. Multimed Tools Appl 63(1):27–44
Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Disc 1(3):241–258
Manning J, McConnell EA (1997) Technology assessment. A framework for generating questions useful in evaluating nursing information systems. Comput Nurs 15(3):141–146
Oh SY, Chung KY (2013) Target speech feature extraction using non-parametric correlation coefficient. Clust Comput. doi:10.1007/s10586-013-0284-5
Oliveira S, Zaiane O, Saygin Y (2004) Secure association rule sharing. In: Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining pp. 74–85
Park IS, Jang M, Yu SA, Kim HG, Oh PJ, Jung HJ (2010) Analysis of pain records using electronic nursing records of hospitalized patients in medical units at a University Hospital. J Korean Clin Nurs Res 16(3):128
Pazzani MJ (1999) A framework for collaborative content-based and demographic filtering. J Artif Intell Rev 13(5):393–408
Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. Proc. of the 10th International Conference on WWW pp. 285–295
Saygin Y, Verykios VS, Elmagarmid AK (2002) Privacy preserving association rule mining. In: Proc. of International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems pp. 151–158
Song CW, Lee D, Chung KY, Rim KW, Lee JH (2013) Interactive middleware architecture for lifelog based context awareness. Multimed Tools Appl. doi:10.1007/s11042-013-1362-7
Sun X, Yu PS (2007) Hiding sensitive frequent itemsets by a border-based approach. J Comput Sci Eng 1(1):74–94
Yoo H (2011) Pain nursing intervention supporting system using collaborative filtering techniques. Master Paper, Sangji University
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This work was supported by the Industrial Strategic technology development program, 10037283, funded By the Ministry of Trade, Industry & Energy (MI, Korea).
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Jung, H., Chung, KY. & Lee, YH. Decision supporting method for chronic disease patients based on mining frequent pattern tree. Multimed Tools Appl 74, 8979–8991 (2015). https://doi.org/10.1007/s11042-013-1730-3
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DOI: https://doi.org/10.1007/s11042-013-1730-3