MEDREADFAST: A structural information retrieval engine for big clinical text

Large scale text mining research, informally called Big text is a crucial part of Big data agenda that recently started gaining momentum [24]. It targets new technologies to manage large amounts of unstructured textual data in order to quickly find an retrieve needed information. In medical domain fast access to information is especially important. Keyword-search, a de-facto standard to search over Electronic Health Records (EHR), being simple and therefore popular technique, however, is not ideal and often returns either too many irrelevant or too few relevant search results. Clinicians, usually very short on time, just cannot afford trial and error of keyword-search and therefore do not use all information available in patient records. Next generation patient care requires more efficient access to valuable information hidden in patient histories represented by millions of patient records. There is abundance of relevant research results in the Semantic Web research community that offers more robust access interfaces to unstructured data compared to keyword-search. Here we describe a new hybrid browser specifically for EHR that offers advanced user experience combining keyword-search with navigation over an automatically inferred hierarchical document index. The internal representation of the browsing index as a collection of UFOs [25] yields more relevant search results and improves user experience.