運用“購物籃分析技術”探討滯留急診超過24小時病患特性

Objectives: Traditionally, the algorism of basket analysis in Data Mining is often used for business marketing, and the combination of products which are purchased together will be explored by large amounts of transaction data; however, this study also applied it to analyzed the characteristic of patients stayed at the emergency department over 24 hours. Methods: 15,454 patients stayed at the emergency department over 24 hours in one medical center were screened from total 85,330 emergency patients in one year duration, and the logistic regression and basket analysis a Data Mining tool were used to analyze attributes of patient such as age, degree of triage, medical specifics, the way of coming, the way of leaving and the disease classification. Results: The results of logistic regression analysis had indicated that the attributes of gender, age, degree of triage, the way of leaving and health expense significantly influenced the status patient stayed over 24 hours or not, and then the basket analysis also produced 20 association items and rules. Conclusion: We found some specific group needed to be managed (for example, child patient during 0 to 4 years old, pregnancy or postpartum complication, elder upper 65 years old suffered from specific disease such as circulatory system disease, endocrine immune disease, congenital abnormal disease). Otherwise, the Basket Analysis also display something abnormal characteristics which were rarely found before and then suggested for further research.