Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes

Current Electronic Medical Records (EMR) systems contain large amounts of texts and various tables, to show numerous health data. This type of presentation limits people from promptly determining medical conditions or quickly finding desired information given the large volume of texts that needs to be read. We aim to tackle this as information visualization and extraction problems by creation of easy and intuitive user interfaces for visualizing medical information. We present both a novel graphical interface for visualizing a summary of medical information and an information extraction system that is able to extract and visualize the patient’s medical information from structured clinical notes. The graphical interface allows spatial-position based representations of medical information on human body images (front and back views) and temporal-time based representation of it through interconnected time axes. Medical histories are classified into several event categories and 6 physiological systems to enable efficient browsing of selected information. To extract visual tags from a given clinical note, we use natural language processing. We employ Metamap of 2014AA knowledge source for medical information extraction. We trained 1294 English clinical notes with a Time-Entity Detection model by Apache Open NLP to abstract the time expressions. Extracted location of illness is assigned into one of 6 physiological systems is displayed in spatial interface while the related data are denoted on a horizontal timeline of temporal interface.