Data Mining in Health Care
Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010).
Benefits of using data mining in health care
Care providers can make use of data analysis in identifying effective treatments and the best practices. This can be achieved through making comparison of causes, symptoms and adverse effects. Data mining can also be used in making analysis of the cause of action that will be effective for a specific group of patients. This can also be used in the identification of best clinical practices hence help in the development of guidelines and standards of care. To patients data mining is useful in that they can receive better and more affordable healthcare services. This is particularly the case when healthcare managers use the data mining applications in the identification and tracking of chronic diseases and design appropriate interventions and reduce the number of admissions made in hospitals as well as claims being made. Healthcare organizations use data mining in order to make better decisions when it comes to their patients. For example data mining provides information that guides patient's interactions by determining the preferences...
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Stated to be barriers in the current environment and responsible for the reporting that is inadequate in relation to medical errors are: Lack of a common understanding about errors among health care professionals Physicians generally think of errors as individual that resulted from patient morbidity or mortality. Physicians report errors in medical records that have in turn been ignored by researchers. Interestingly errors in medication occur in almost 1 of every 5 doses
The tools used, in this case, for knowledge discovery and data mining where based on artificial neural networks (ANN) and consisted of four different models. All models represented supervised learning models with a known output. The four models of the ANN were dynamic network, prune network, the multilayer perceptron, and the radial basis function network. The main challenge for its implementation was that data needed to be cleaned so the data
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The term "data mining" refers to the process of taking a large amount of data, sorting through it, and refining appropriate and relevant information. Typically, it is used with ERP systems to help make strategic decisions based on the patterns that come out of the data review. It is also that more formal and statistical analysis of large sets of data within complex ERP Systems. Data warehousing is a term
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