Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

Research on Care of Postoperative Patient based on Rough Sets Theory

Print
PDF
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Authors:
Yunlong Xu
Yuping Cao
Songlin Yang
10.5120/3859-5383

Yunlong Xu, Yuping Cao and Songlin Yang. Article:Research on Care of Postoperative Patient based on Rough Sets Theory. International Journal of Computer Applications 31(10):8-12, October 2011. Full text available. BibTeX

@article{key:article,
	author = {Yunlong Xu and Yuping Cao and Songlin Yang},
	title = {Article:Research on Care of Postoperative Patient based on Rough Sets Theory},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {31},
	number = {10},
	pages = {8-12},
	month = {October},
	note = {Full text available}
}

Abstract

In this paper, the rough set theory is used to deal with the relations between patient’s temperature, oxygen saturation, blood pressure and decision ADM-DECS in care of postoperative patients, we select 28 postoperative patients as a sample, take ADM-DECS as a decision attribute and take patient’s temperature, oxygen saturation, blood pressure as condition attributes. We are based on rough-set theory to research importance of condition attributes with respective to decision attribute and strength of condition attributes supporting decision attribute. Results of this research will be helpful for nurses to raise quality of care.

Reference

  • G. Alvatore, M. Bentto and S. Roman, Rough set theory for multi criteria decision analysis, European Journal of Operational Research 129(2001), 1-47.
  • A. Budihardjo, J. Grzymala-Busse and L. Woolery, Program LERS-LB 2.5 as a tool for knowledge acquisition in nuesing, Proceeding of the 4th Int. Conference on Industrial & Engineering Applicati- ons of AI&Expert Sytems, (1991), 735-740.
  • R. Golan and W. Ziarko, Methodology for stock market analysis utilizing rough set theory, Proc. of IEEE/IAFE Conference on Computational Intellig- ence for Financial Engineering 22(1995), 32-40.
  • Q. Li,G. Xie and Y. MU, The application of data mining technology based on rough sets to medical diagnosis, CHINESE MEDICAL EQUIPMENT JOURNAL, 3(2005), 3-7.
  • Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences 11(1982), 341-356.
  • Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, 1991.
  • Z. Pawlak, Vagueness and uncertainty: a rough set perspective, Computational Intelligence 11(1995), 227-232.
  • Pawlak, Z., Decision rules and flow networks, Eur- opean Journal of Operational Research 154, 2004, 184-190.
  • S. Padmini and H. Donald, Vocabulary mining for information retrieval: rough sets and fuzzy sets, Information Processing and Management 37(2002), 15-38.
  • K. Qin, Y. Gao and Z. Pei, On covering rough sets, LNAI 4481(2007), 34-41.
  • S. Tsumoto, Automated discovery of medical expe- rt system rules from clinical databases based on rough set, Proc. of Second International Conf. on Knowledge discovery and data Mining, 32(1996), 63-72.
  • M. Yahia, R. Mahmodr and N. Sulaimann, Rough neural expert systems, Expert system with Applications 18(2002), 87-99.