Constructing a Data Mining Sensor to Classify Tw-DRGs Medical Specialties

Yuan Huei Huang; Che Wei Chang

doi:10.4028/www.scientific.net/AMM.263-266.969

Paper Titles

An Improved Algorithm of LEACH Routing Protocol Based on Energy-Balancing
p.948

An Ant Colony Optimization Approach to Power Allocation in Wireless Sensor Networks
p.954

A Reliable Wireless Sensor Network Routing Scheme for Environmental Monitoring System
p.959

Calibration Based on a Simplified D-ToA Localization Method for the Video Sensor Nodes on the Metro Vehicle
p.963

Constructing a Data Mining Sensor to Classify Tw-DRGs Medical Specialties
p.969

A MAP Selection Scheme Based on Multi-Attribute Decision Making in Hierarchical MIPv6
p.975

A Novel Low-Voltage CMOS Mixer with Differential Floating-Gate Input
p.980

An Improved Ant Colony Optimization RWA Algorithm and its Application in Optical Network Wavelength Converter Allocation Strategy
p.986

Implementation of Costas Loop for BPSK Receivers Using FPGA
p.990

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266Constructing a Data Mining Sensor to Classify...

Constructing a Data Mining Sensor to Classify Tw-DRGs Medical Specialties

Abstract:

This work applies decision tree analysis to construct a classification sensor for Taiwan’s diagnosis related groups (Tw-DRGs) of medical specialties. Inpatient expenses of 2009 in a national health insurance database were transformed to the SQL database analytic software for analysis and classification. DRG code is selected by the principal diagnosis code with the operating room procedures code. Selecting the combinations in accordance with National Health Insurance inpatient diagnostic related groups TW-DRG Classification Manual, resulting in repeated DRG codes. The study analyzes three medical classifications of Tw-DRGs implementations on 2009 to solve the DRG code appropriate attribution issues. Appropriateness and accuracy of the DRG code is an important influence to the medical payments. The results found that decision tree can effectively and quickly establish the DRG classification process.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

969-972

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.969

Citation:

Cite this paper

Online since:

December 2012

Authors:

Yuan Huei Huang*, Che Wei Chang

Keywords:

Data Mining (DM), Decision Tree Analysis, Structured Query Language, Tw-DRGs

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Vladeck BC., Hospital prospective payment and the quality of care. New England Journal Medicine (1988), 319:1411–3.

DOI: 10.1056/nejm198811243192109

Google Scholar

[2] Wiley M.M., DRGs as a basis for prospective payment. Health Policy (1988),9:157–65.

Google Scholar

[3] Pieter Adriaans , Dolf Zantinge, "Data Mining", Addison-Wesley Professional (1996).

Google Scholar

[4] David C. Chou and Amy Y. Chou "A MANAGER'S GUIDE TO DATA MINING", Information Systems Management Fall Volume 16, Issue 4(1999), pp.1-9.

Google Scholar

[5] Berry, M. J. A. and Linoff, G., Data Mining Technique for Marketing, Sale, and Customer Support, Wiley Computer, (1997).

Google Scholar

[6] Quinlan, J. R., "C4.5: Programs for Machine Learning," Morgan Kaufmann, San Mateo: CA,(1993)

Google Scholar

[7] Ron Kohavi , Ross Quinlan,C5.1.3. Decision Tree Discovery (1999).

Google Scholar

[8] Hunt, E. B., Marin, J., and Stone, P. J.,"Experiments in Induction".Academic Press,New York(1966).

Google Scholar

[9] J. R. Quinlan. Induction of decision trees. Machine Learning, (1986) 1:81–106.

Google Scholar