Abstract
By analyzing the current status and problems of hospital medical data service, to propose the data cleaning scheme, and construct the database with the subject of disease expense to meet the requirements of further data mining. By analyzing costs of patients and the relationship of hospitals, diseases, surgeries, to establish a multilevel and three-dimensional analysis framework to study the current situation and characteristics of renal transplantation patients’ costs based on this framework. By using data mining technology, this paper tries to find out whether there are different rules of medical treatment behavior in the use of drugs and sanitary materials in the diagnosis and treatment of renal transplantation patients, and the current cost situation under different rules, and to explore the main factors affecting the cost of patients, so as to provide new ideas for the management and control of disease cost.
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References
Bentall, Andrew, Desley Neil, Adnan Sharif, and Simon Ball. 2015. ABO-incompatible kidney transplantation is a novel risk factor for BK nephropathy. Transplantation 2: 195–201.
Warejko, Jillian K., and S. Paul Hmiel. 2014. Single‐center experience in pediatric renal transplantation using thymoglobulin induction and steroid minimization. Pediatric Transplantation 8: 939–942.
Al Aghbari, Zaher. 2004. Array-index: A plug&search K nearest neighbors method for high-dimensional data. Data & Knowledge Engineering 3: 239–241.
Melab, Nordine. 2001. Data mining: A key contribution to E-business. Information & Communications Technology Law 3: 468–472.
Boratyńska, M., A. Wakulenko, M. Klinger, and P. Szyber. 2014. Chronic allograft dysfunction in kidney transplant recipients: Long-term single-center study. Transplantation Proceedings 8: 755–756.
Remport, Adam, Andras Keszei, and Eszter Panna Vamos. 2011. Association of pre-transplant dialysis duration with outcome in kidney transplant recipients: A prevalent cohort study. International Urology and Nephrology 4: 415–418.
Hsu, J.H., C.S. Tseng, and S.C. Chen. 2001. A methodology for evaluation of boundary detection algorithms on breast ultrasound images. Journal of Medical Engineering & Technology 4: 459–462.
Che, Dongsheng, Qi Liu, Khaled Rasheed, and Xiuping Tao. 2011. Decision tree and ensemble learning algorithms with their applications in bioinformatics. Advances in Experimental Medicine and Biology 3: 365–371.
Lim, Wai H., Sean Chang, Steve Chadban, Scott Campbell, Hannah Dent, Graeme R. Russ, and Stephen P. McDonald. 2010. Donor-recipient age matching improves years of graft function in deceased-donor kidney transplantation. Nephrology Dialysis, Transplantation 25 (9): 3082–3089. Official publication of the European Dialysis and Transplant Association - European Renal Association 2, 142–146.
Patricia, B. 2007. Cerrito: Choice of antibiotic in open heart surgery. Intelligent Decision Technologies 1: 89–92.
Yang, Wan-Shiou, and San-Yih Hwang. 2005. A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications 1: 143–148.
Wang, Louis W., Philip Masson, Robin M. Turner, Stephen W. Lord, Laura A. Baines, Jonathan C. Craig, and Angela C. Webster. 2015. Prognostic value of cardiac tests in potential kidney transplant recipients: A systematic review. Transplantation 4: 724–728.
Shu, Wanneng, and Lixing Ding. 2011. ECOGA: Efficient data mining approach for fuzzy association rules. Journal of Software 1: 93–94.
Baigent, C., L. Blackwell, R. Collins, J. Emberson, J. Godwin, R. Peto, J. Buring, et al. 2009. ‘Antithrombotic Trialists’ (ATT) collaboration: Aspirin in the primary and secondary prevention of vascular disease: Collaborative meta-analysis of individual participant data from randomised trials. The Lancet 373 (9678): 1849–1860.
Hemant, M. 2005. Phatak: Retrospective detection of potential medication errors involving drugs with similar names. Journal of the American Pharmacists Association 5: 639–642.
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This research was supported by the First Hospital of Jilin University.
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He, K., Wang, J., Wang, J., Wang, N. (2020). Cost of Kidney Transplantation on the Base of Data Mining Technology. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_7
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DOI: https://doi.org/10.1007/978-981-15-1468-5_7
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