![]() ![]() ![]() The latter are usually associated with qualitative evaluation.Ĭlinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria.Ĭlinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. The nine data mining steps proposed by Fayyad in 1996 were used as the main themes of the review. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead. Clinical data mining is the application of data mining techniques using clinical data. ![]()
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