ArticleJournal of medical systems2013
Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction.
Article in Journal of medical systems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 54 citations in OpenAlex.
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- A comprehensive risk factor analysis using association rules in people with diabetic kidney disease.Scientific reports · 2023Article
- Low Trend for VZV-Associated Disease Patients to Visit Neurologists.Journal of multidisciplinary healthcare · 2023Article
- Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method.Healthcare (Basel, Switzerland) · 2021Article
- Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset.Healthcare informatics research · 2020Article
- The Impact of BMI Categories on Metabolic Abnormality Development in Chinese Adults Who are Metabolically Healthy: A 7-Year Prospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2020Article
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- The index lift in data mining has a close relationship with the association measure relative risk in epidemiological studies.BMC medical informatics and decision making · 2019Article
- Use of oximetry as a screening tool for obstructive sleep apnea: a case study in Taiwan.Journal of medical systems · 2015Article
- Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events.Applied clinical informatics · 2015Article
- An association rule mining-based framework for understanding lifestyle risk behaviors.PloS one · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The knowledge discovery has been widely applied to mine significant knowledge from medical data. Nevertheless, previous studies have produced large numbers of imprecise patterns. To reduce the number of imprecise patterns, we need an approach that can discover interesting patterns that connote causality between antecedent and consequence in a pattern. In this paper, we propose association rule mining method that can discover interesting patterns that include medical knowledge in Korean acute myocardial infarction registry that consists of 1,247 young adults collected by 51 participating hospitals since 2005. Proposed method can remove imprecise patterns and discover target patterns that include associations between blood factors and disease history. The association that blood factors affect to disease history is defined as target pattern. In our experiments, the interestingness of a target pattern is evaluated in terms of statistical measures such as lift, leverage, and conviction. We discover medical knowledge that glucose, smoking, triglyceride total cholesterol, and creatinine are associated with diabetes and hypertension in Korean young adults with acute myocardial infarction.
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