Summarization of Patient Groups Using the Fuzzy C-Means and Ontology Similarity Measures

This paper addresses the problem of constructing a summarization of groups of patients that are found by clustering a hospital database where diagnoses are encoded in a controlled medical vocabulary, called ICD-9. Our method finds the "most representative terms" (MRTs) for a patient cluster by using weights from a fuzzy partition matrix generated by fuzzy clustering the patient similarity matrix. We present a novel approach to computing patient similarity by using OWA operators. Finally, we apply our method to a set of 2077 cardiology patients.