Discovering Medical Knowledge using Association Rule Mining in Young Adults with Acute Myocardial Infarction

Lee, Dong Gyu; Ryu, Kwang Sun; Bashir, Mohamed Ezzeldin A.; Bae, Jang‐Whan; Ryu, Keun Ho

doi:10.1007/s10916-012-9896-1

Cited by 42 publications

(25 citation statements)

References 31 publications

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“…For the experimental setting, Ordonez 23 used arteries data and found out the association rules for healthy and diseased arteries. In their study, Lee et al 24 proposed ARM method which was able to discover interesting patterns including a medical data in Korean acute myocardial infarction registry where data were collected by 51 participating hospitals. The performances of target pattern were evaluated in terms of statistical measures such as lift, leverage, and conviction.…”

Section: Related Studiesmentioning

confidence: 99%

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Sarıyer

2019

Health Informatics J

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Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However, since these tests are expensive and time-consuming, ordering correct tests for patients is crucial for efficient use of hospital resources. Thus, understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease, and laboratory tests were grouped into four main categories (hemogram, biochemistry, cardiac enzyme, urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values, higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient’s diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients.

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Section: Related Studiesmentioning

confidence: 99%

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Sarıyer

2019

Health Informatics J

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“…Usually, data mining tools enable users to determine thresholds for the rules, but afterwards, there is no best approach for selecting effective rules. During the evaluation of the rules, users may deal with huge amount of rules and search some strategies to find good ones [32,33].…”

Section: Selection Of Meaningful Association Rulesmentioning

confidence: 99%

Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

Yıldırım¹

2015

Appl Clin Inform

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SummaryBackground: Ciprofloxacin is one of the main drugs to treat bacterial infections. Bacterial infections can lead to high morbidity, mortality, and costs of treatment in the world. In this study, an analysis was conducted using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database on the adverse events of ciprofloxacin. Objectives: The aim of this study was to explore unknown associations among the adverse events of ciprofloxacin, patient demographics and adverse event outcomes. Methods: A search of FDA AERS reports was performed and some statistics was highlighted. The most frequent adverse events and event outcomes of ciprofloxacin were listed, age and gender specific distribution of adverse events are reported, then the apriori algorithm was applied to the dataset to obtain some association rules and objective measures were used to select interesting ones. Furthermore, the results were compared against classical data mining algorithms and discussed. Results: The search resulted in 6 531 reports. The reports included within the dataset consist of 3 585 (55.8%) female and 2 884 (44.1%) male patients. The mean age of patients is 54.59 years. Preschool child, middle aged and aged groups have most adverse events reports in all groups. Pyrexia has the highest frequency with ciprofloxacin, followed by pain, diarrhoea, and anxiety in this order and the most frequent adverse event outcome is hospitalization. Age and gender based differences in the events in patients were found. In addition, some of the interesting associations obtained from the Apriori algorithm include not only psychiatric disorders but specifically their manifestation in specific gender groups. Conclusions: The FDA AERS offers an important data resource to identify new or unknown adverse events of drugs in the biomedical domain. The results that were obtained in this study can provide valuable information for medical researchers and decision makers at the pharmaceutical research field.

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“…Immamura et al [11] utilized an association rules technique on 477 patients in order to investigate the three most useful clinical findings for chronic diseases. Lee et al [12] have proposed an association rule mining method on 1,247 young Korean adults to extract patterns related to acute myocardial infarction. Still, most commercial DSSs focus mainly on extracting statistical measurements from a given patient database and not on extracting new knowledge.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for the Treatment of Patients with Ventricular Assist Device Support

Karvounis¹,

Tsipouras²,

Tzallas³

et al. 2014

Methods Inf Med

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SummaryBackground: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance.Objectives: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an inter-operable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients’ treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist’s Decision Support System (SDSS), an innovative component of the SensorART platform.Methods: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients’ data, extracting new knowledge, and making informative decisions.Results: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection.Conclusions: SDSS and its modules have been tested in a number of patients and the results are encouraging.

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Discovering Medical Knowledge using Association Rule Mining in Young Adults with Acute Myocardial Infarction

Cited by 42 publications

References 31 publications

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

A Decision Support System for the Treatment of Patients with Ventricular Assist Device Support

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