천식 환자의 응급실을 통한 입원: 연관성 규칙 마이닝
Hospitalization through Emergency Department Visit in Patients with Asthma: Association Rule Mining Approach
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Objectives: To explore the patients’ characteristics associated with hospitalization through emergency department (ED) visit with asthma exacerbation by using association rule mining (ARM) Methods: A retrospective cross-sectional study was performed using ED visit cases from National Emergency Department Information System database 2016. In 2016, 9127979 ED visit cases collected. We included ED visit cases with discharge diagnosis of asthma (ICD-10 code of J45/J46). ARM was performed with Apriori algorithm to explore association rules between patients’ characteristics and hospitalization through ED visit. Hospitalization includes admissions to general ward and intensive care unit. The association rules (A⇒B) meant that if a person has a feature A, a person also has a feature B (hospitalization). We used support, confidence, and lift to select interesting rules. Support was defined as the proportion of an itemset in the data. Confidence was used as a measure of reliability. Lift was used to estimate whether the occurrences of A and B were independent or not. The higher confidence, the more likely to have an interesting relation between A and B. The higher lift (lift >1), the more likely that there was a dependency between A and B. We eliminated redundant rules that have similar meanings. Results: We included 28179 ED visit cases for the analysis. We extracted association rules with a support ≥0.5%, a confidence ≥80% and a lift >1. After eliminating redundant rules, the rule with the highest confidence and lift was the rule ({over-80-year-old, female, comorbid with pneumonia}⇒hospitalization) with confidence: 80.87% and lift: 2.94. Conclusion: Among patients visited the ED with asthma exacerbation, combination of elderly, female, and comorbid with pneumonia was importantly associated with hospitalization.
Abstract
Objectives: To explore the patients’ characteristics associated with hospitalization through emergency department (ED) visit with asthma exacerbation by using association rule mining (ARM) Methods: A retrospective cross-sectional study was performed using ED visit cases from National Emergency Department Information System database 2016. In 2016, 9127979 ED visit cases collected. We included ED visit cases with discharge diagnosis of asthma (ICD-10 code of J45/J46). ARM was performed with Apriori algorithm to explore association rules between patients’ characteristics and hospitalization through ED visit. Hospitalization includes admissions to general ward and intensive care unit. The association rules (A⇒B) meant that if a person has a feature A, a person also has a feature B (hospitalization). We used support, confidence, and lift to select interesting rules. Support was defined as the proportion of an itemset in the data. Confidence was used as a measure of reliability. Lift was used to estimate whether the occurrences of A and B were independent or not. The higher confidence, the more likely to have an interesting relation between A and B. The higher lift (lift >1), the more likely that there was a dependency between A and B. We eliminated redundant rules that have similar meanings. Results: We included 28179 ED visit cases for the analysis. We extracted association rules with a support ≥0.5%, a confidence ≥80% and a lift >1. After eliminating redundant rules, the rule with the highest confidence and lift was the rule ({over-80-year-old, female, comorbid with pneumonia}⇒hospitalization) with confidence: 80.87% and lift: 2.94. Conclusion: Among patients visited the ED with asthma exacerbation, combination of elderly, female, and comorbid with pneumonia was importantly associated with hospitalization.
참고문헌 (9)
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