Hypoplastic left heart syndrome: knowledge discovery with a data mining approach

Kusiak, Andrew; Caldarone, Christopher A.; Kelleher, Michael D.; Lamb, Fred S.; Persoon, Thomas J.; Burns, Alex

doi:10.1016/j.compbiomed.2004.07.007

Cited by 22 publications

(9 citation statements)

References 9 publications

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“…Other methods of knowledge creation include a data mining approach for pediatric cardiac issues [Kusiak et al, 2006], and a combined data mining and ANN system using bedside monitoring data as intermediate outcome data [Silva et al, 2008]. A rules-based system proved successful at generating intelligent alarms in cardio-anesthesia monitoring [Popp et al, 1991].…”

Section: Knowledge Creation Systems: Monitoringmentioning

confidence: 99%

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Wills¹,

Sarnikar²,

El‐Gayar³

et al. 2010

CAIS

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Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technologybased KM approaches.

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Section: Knowledge Creation Systems: Monitoringmentioning

confidence: 99%

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Wills¹,

Sarnikar²,

El‐Gayar³

et al. 2010

CAIS

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“… Management: Kusiak et al developed a metric for analyzing data and assessing quality measurement and its benefits [6].…”

Section: Introductionmentioning

confidence: 99%

Weighted Association Rules and Scoring Methodology for Cardiovascular Diseases

Goy¹,

Kolukisa²,

Bakır-Güngör³

et al. 2019

IJBBB

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Cardiovascular diseases (CVD), including coronary artery disease (CAD), myocardial infarction, and stroke are a group of highly prevalent and deadly diseases. The deaths from cardiovascular diseases were announced as 17.9 million in 2016 and it is expected that this number will reach approximately to 23.6 million by 2030. In order to facilitate the diagnosis and treatment of CVD, several computational approaches and data mining methods have been proposed until now. In this study, Apriori algorithm is utilized to find associations between features and rules based on UCI's publicly available Cleveland dataset. Additionally, we generate different weighted association rules, which can help medical doctors to stratify patients and thus, propose different treatment approaches for each patient's sub-category. Performance results show that the Apriori algorithm creates 58 rules when support and confidence parameters are set to 0.1 and 0.9, respectively. Utilizing weighted association rule approach, 6 important rules have been created based on Clinical Important Factors (CIF) and Framingham Heart Study Risk factors (FHS RF) on CVD.

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“…This search procedure is iterative, and usually come to an end when rules generalization is no longer or some other alternate criteria like noise may rise [6]. Decision rules are always follows IF (Condition=Yes) THEN (Result) [7] Alcoholic fatty liver disorder is happen due to the excessive consumption of alcohol and it is a spectrum of injury to the liver starting from simple steatosis to cirrhosis [8]. Non-alcoholic fatty liver disease (NAFLD) is a common pathological condition categorized by solemn lipid deposition in the hepatocytes of the liver parenchyma [9].…”

Section: Introductionmentioning

confidence: 99%

Reverse Sequential Covering Algorithm for Medical Data Mining

Aneeshkumar

Venkateswaran

2015

Procedia Computer Science

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Sequential Covering approach often follows general to specific approach to identify essential tuples and which are used to form a learning rule. Like other fields interpretation of sequential cover is more effective in health care also. Reverse technique will help to read instances from the last to generate appropriate rules. Alcoholic fatty liver disorder and Non-alcoholic fatty liver disorder dataset is used in this article for rule learning.

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Hypoplastic left heart syndrome: knowledge discovery with a data mining approach

Cited by 22 publications

References 9 publications

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Weighted Association Rules and Scoring Methodology for Cardiovascular Diseases

Reverse Sequential Covering Algorithm for Medical Data Mining

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