title: A Case-based Retrieval System using Natural Language Processing and Population-based Visualization.
abstract: Electronic medical records capture large quantities of patient data generated as a result of routine care. Secondary use of this data for clinical research could provide new insights into the evolution of diseases and help assess the effectiveness of available interventions. Unfortunately, the unstructured nature of clinical data hinders a user's ability to understand this data: tools are needed to structure, model, and visualize the data to elucidate patterns in a patient population. We present a case-based retrieval framework that incorporates an extraction tool to identify concepts from clinical reports, a disease model to capture necessary context for interpreting extracted concepts, and a model-driven visualization to facilitate querying and interpretation of the results. We describe how the model is used to group, filter, and retrieve similar cases. We present an application of the framework that aids users in exploring a population of intracranial aneurysm patients.
authors: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors: William
authors: W
authors: Hsu
authors: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors: Ricky K
authors: RK
authors: Taira
authors: Division of Interventional Neuroradiology, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors: Fernando
authors: F
authors: Viñuela
authors: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA.
authors: Alex A T
authors: AA
authors: Bui
keywords: Case-based retrieval; information visualization; knowledge reprsentation; natural language processing