  Over the past decade, there has been a steep rise in the data-driven analysis
in major areas of medicine, such as clinical decision support system, survival
analysis, patient similarity analysis, image analytics etc. Most of the data in
the field are well-structured and available in numerical or categorical formats
which can be used for experiments directly. But on the opposite end of the
spectrum, there exists a wide expanse of data that is intractable for direct
analysis owing to its unstructured nature which can be found in the form of
discharge summaries, clinical notes, procedural notes which are in human
written narrative format and neither have any relational model nor any standard
grammatical structure. An important step in the utilization of these texts for
such studies is to transform and process the data to retrieve structured
information from the haystack of irrelevant data using information retrieval
and data mining techniques. To address this problem, the authors present Q-Map
in this paper, which is a simple yet robust system that can sift through
massive datasets with unregulated formats to retrieve structured information
aggressively and efficiently. It is backed by an effective mining technique
which is based on a string matching algorithm that is indexed on curated
knowledge sources, that is both fast and configurable. The authors also briefly
examine its comparative performance with MetaMap, one of the most reputed tools
for medical concepts retrieval and present the advantages the former displays
over the latter.
