Exploratory Analysis of Electronic Health Records using Topic Modeling



The rapid growth of electronic health record (EHR) systems brings the increase of available information about patients in hospitals. This massive amount of text information represents an opportunity to extract unknown information about medical history, medication, diseases, allergies, among others. Extract the main topics that represent
the subjects covered by a text collection can give valuable insights. To this end, approaches for topic modeling have been used to tackle such problems of information discovery and extracting topic with thematic information. In this sense, this work presents an exploratory analysis of a health collection of electronic records from an intensive care unit (ICU). The collection is split into two sub-collections: discharged patients and patients who progressed to death. We apply an LDA-based approach to discover the latent topics from the collections. The analyses show that some topics are more recurrent in the death collection, like renal diseases, and others are more recurrent in discharge collection, like, diabetes. The results of the analyses can be useful for improving the health intensive care services since the topics can be a guide to understand the patterns in discharge and death situations.


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