American Journal of Applied Sciences

Generating Treatment Plan in Medicine: A Data Mining Approach

Ahmad Mahir Razali and Shahriyah Ali

DOI : 10.3844/ajassp.2009.345.351

American Journal of Applied Sciences

Volume 6, Issue 2

Pages 345-351

Abstract

This study reports on a research effort on generating treatment plan to handle the error and complexity of treatment process for healthcare providers. Focus has been given for outpatient and was based on data collected from various health centers throughout Malaysia. These clinical data were recorded using SOAP (Subjective, Objective, Assessment and Plan) format approach as being practiced in medicine and were recorded electronically via Percuro Clinical Information System (Percuro). Cross-Industry Standard Process for Data Mining (CRISP-DM) model has been utilized for the entire research. We used data mining analysis through decision trees technique with C5 algorithm. The scopes that have been set are patient’s complaint, gender, age, race, type of plan and detailed item given to patient. Acute upper respiratory infection disease or identified as J06.9 in International Classification of Diseases 10 by World Health Organization has been selected as it was the most common problem encountered. The model created for J06.9 disease is that type of plan recommended through giving drug to patients without the need to consider patient’s complaint, gender, age and race, with accuracy obtained for the model is 94.73%. Inspite of that, we also identified detailed items that have been given to J06.9 patients and the occurancy of them. This can be as a guideline for future treatment with item recommendation is less than 0.078% compared to item inventory in Percuro database. The research is expected to aid healthcare provider as well as to minimize error during treatment process while benefited from technology information to increase the health care delivery.

Copyright

© 2009 Ahmad Mahir Razali and Shahriyah Ali. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.