Extracting Association Rules from Emergency Department Data

Ivașcu, Todor; Cincar, Kristijan; Carunta, Alina

doi:10.1109/ehb47216.2019.8969967

Cited by 1 publication

(2 citation statements)

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“…They used the standard mlxtend package to aggregate data by cluster, user login, and time for analysis. They were confronted with the problem of data inaccuracy and consistency with real-world settings while working, and were obliged to limit the minimal support for associative rules 16 . The Apriori approach used to create association rules using data obtained during emergency department visits, either through triage or electronic health records.…”

Section: Introductionmentioning

confidence: 99%

“…The Apriori technique was used to derive the association rules from a real dataset of visits to emergency rooms. This study offered critical info on the correlations between various data elements from a large quantity of patient data, allowing medical workers to look for other related concerns that the patient may not have reported 16 . This study intended to investigate disease association for other patient categories in the future, as well as incorporating other factors.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on Association Rule Mining Algorithms on the Hospital Infection Control Dataset

2023

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Administrative procedures in various organizations produce numerous crucial records and data. These records and data are also used in other processes like customer relationship management and accounting operations.It is incredibly challenging to use and extract valuable and meaningful information from these data and records because they are frequently enormous and continuously growing in size and complexity.Data mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future trends and make better business decisions.The Apriori algorithm has been introduced to calculate the association rules between objects; the primary goal of this algorithm is to establish an association rule between various things. The association rule describes how two or more objects are related.We have employed the Apriori property and Apriori Mlxtend algorithms in this study and we applied them on the hospital database; and, by using python coding, the results showed that the performance of Apriori Mlxtend was faster, and it was 0.38622, and the Apriori property algorithm was 0.090909. That means the Apriori Mlxtend was better than the Apriori property algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%