Extracting Accident Event Aviso Rules by Soft Computing Techniques in the Healthcare Industry
- DOI
- 10.2991/iwcss-13.2013.48How to use a DOI?
- Keywords
- Accident Event Aviso System (AEAS); decision tree; Cumulative Probability Distribution Approach (CPDA); Rough Set Theory (RST)
- Abstract
The hospitalized safety environment of patients is a priority-concerned by the healthcare services for ensuring patient safety and healthcare quality during inpatient admission in hospitals. Particularly, the applications of accident event aviso system (AEAS) and knowledge discovery for identifying accident event aviso category (AEAC) will have assistance to determine potential hazards and reduce medical errors to enhance patient safety and healthcare quality issues in such a complexity healthcare system of hospitals and are vitally important in the healthcare industry. Thus, this work proposes a hybrid procedure to highlight such an interesting issue. The proposed procedure constitutes five components, expert feature-screening approach, decision tree C4.5 algorithm, cumulative probability distribution approach (CPDA), the rough sets LEM2 algorithm, and rule filter technique. An experimental dataset was retrieved from the AEAS of existing hospital databases. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes, achieving increased classification accuracy, providing a knowledge-based aviso system, and providing sufficient information to solving AEAC problems encountered by patients and hospitals, thereby benefiting interested parties.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - You Shyang Chen PY - 2013/08 DA - 2013/08 TI - Extracting Accident Event Aviso Rules by Soft Computing Techniques in the Healthcare Industry BT - Proceedings of the 2013 International Workshop on Computer Science in Sports PB - Atlantis Press SP - 177 EP - 181 SN - 1951-6851 UR - https://doi.org/10.2991/iwcss-13.2013.48 DO - 10.2991/iwcss-13.2013.48 ID - Chen2013/08 ER -