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Challenges in Data Mining on Medical Databases

Challenges in Data Mining on Medical Databases
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Author(s): Fatemeh Hosseinkhah (Howard University Hospital, USA), Hassan Ashktorab (Howard University Hospital, USA), Ranjit Veen (American University, USA) and M. Mehdi Owrang O. (American University, USA)
Copyright: 2009
Pages: 12
Source title: Database Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Erickson (University of Nebraska, Omaha, USA)
DOI: 10.4018/978-1-60566-058-5.ch083

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Abstract

Modern electronic health records are designed to capture and render vast quantities of clinical data during the health care process. Technological advancements in the form of computer-based patient records software and personal computer hardware are making the collection of and access to health care data more manageable. However, few tools exist to evaluate and analyze this clinical data after it has been captured and stored. Evaluation of stored clinical data may lead to discovery of trends and patterns hidden within the data that could significantly enhance our understanding of disease progression and management. A common goal of the medical data mining is the detection of some kind of correlation, for example, between genetic features and phenotypes or between medical treatment and reaction of patients (Abidi & Goh, 1998; Li et al., 2005). The characteristics of clinical data, including issues of data availability and complex representation models, can make data mining applications challenging.

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