運用“購物籃分析技術”探討滯留急診超過24小時病患特性

Apply Basket Analysis to Explore Characteristic of Patients Stayed Emergency Department Over 24 Hours

周歆凱(Hsin-Kai Chou) ； 張怡秋(I-Chiu Chang) ； 黃興進(Hsin-Ginn Hwang) ； 蔡明足(Ming-Tsu Tsai) ； 翁林仲(Lin-Chung Woung) ； 蘇喜

《醫務管理期刊》 8卷3期 (2007/09) Pp. 216-231

https://doi.org/10.6174/JHM2007.8(3).216

摘要

目的：傳統上，購物籃分析多利用在市場行銷上，是一種資料探勘技術中『關聯規則』的演算法；簡單來說該方法可藉由分析大量的市場交易紀錄，從中找到消費者會同時購買的商品項目組合。然而，本研究也期望從大量病患就診資料庫中分析滯留急診超過24小時病患的特質及就醫屬性之組合，以探討病患滯留時間及其就醫屬性之關係。
方法：本研究收集某醫學中心急診室一年病患資料共111,514筆，其中滯留急診超過24小時佔18.1%共15,454筆樣本納入本研究。除了一般統計分析外並利用資料探勘技術中之購物籃分析工具將年齡、檢傷分級、科別、來院方式、離院後動向、系統疾病分類等變項投入，嘗試找出滯留急診超過24小時病患之病患屬性及就醫屬性的組合。
結果：經由邏輯斯迥歸分析結果得知性別、年齡、檢傷分級、離院後動向、醫療費用均顯著影響病患滯留急診是否超過24小時；同時購物籃分析中也產生20種關聯組合及規則。
結論：本研究發現某些特殊族群是重點管理的對象，如0~4歲的兒科病人、因懷孕或產後合併症就醫的產科病人、65歲以上之中老年因特殊疾病
（如循環系统疾病、内分泌免疫系统疾病、先天異常疾病）至急诊就署；此外，購物籃分析更探勘出多組異常罕见的特性組合可供爲未来研究之方向。

關鍵字

急診醫學 ； 資料探勘 ； 關聯规則 ； 購物籃分析

並列摘要

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.

並列關鍵字

Emergency Medicine ； Data Mining ； Association Rule ； Basket Analysis

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