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Clinical documents from electronic patient record system has rich data which addresses the details about patient's disease, injuries, medication response. Patient's data in the clinical documents are clustered into groups based on conditions (symptoms names) and medical response (medication/drug names). The documents with patients who are affected with disease, not affected, and are in hypothetical...
Health care data collections are usually characterized by an inherent sparseness due to a large cardinality of patient records and a variety of medical treatments usually adopted for a given pathology. Innovative data analytics approaches are needed to effectively extract interesting knowledge from these large collections. This paper presents an explorative data mining approach, based on a density-based...
A recommendation engine for disease prediction that combines clustering and association analysis techniques is proposed. The system produces local prediction models, specialized on subgroups of similar patients by using the past patient medical history, to determine the set of possible illnesses an individual could develop. Each model is generated by using the set of frequent diseases that contemporarily...
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