Q-Map: clinical concept mining with phrase sense disambiguation
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Over the past decade, there has been a steep rise in data driven analysis in major areas of medicine, such as, clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Also, there are various ongoing research efforts in the operational and financial fields using techniques such as demand forecasting, convex optimization. Most of the data used in these research applications are well-structured and available in numerical or categorical formats which can be used for experiments directly. On the opposite end, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature. These can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written free text format and neither have any relational model nor any standard grammatical structure. An important step in 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. The unregulated format coupled with massive size of datasets makes the mining process a monumental task requiring robust algorithms supported by ample hardware resources and computing power. In this paper, we present Q-Map, which is a simple yet powerful system that can sift through these datasets to retrieve structured information aggressively and efficiently. It is backed by an effective mining algorithm based on curated knowledge sources, that is both fast and configurable. We also present its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval.
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