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KMID : 1146320190070020134
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Journal of Health Technology Assessment
2019 Volume.7 No. 2 p.134 ~ p.138
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Hospitalization through Emergency Department Visit ?in Patients with Asthma: Association Rule Mining Approach
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Han Sol-A
Suh Hae-Sun
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Abstract
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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 NationalEmergency 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). ARMwas performed with Apriori algorithm to explore association rules between patients’ characteristicsand 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 alsohas 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 ofreliability. Lift was used to estimate whether the occurrences of A and B were independent or not. Thehigher 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 redundantrules 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 eliminatingredundant 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.
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KEYWORD
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Asthma, Hospitalization, Emergency department, Data mining, Association
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