Abstract
BackgroundElectronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). The definition of an HOI is generally considered a complex and time consuming task for health care professionals.MethodsIn our clinical note-based pharmacovigilance research, we often operate upon potentially hundreds of ontologies at once, expand query inputs, and we also increase the search space over clinical text as well as structured data. Such a method implies to specify an initial set of seed concepts, which are based on concept unique identifiers. This paper presents a novel method based on Formal Concept Analysis (FCA) and Semantic Query Expansion (SQE) to assist the end-user in defining their seed queries and in refining the expanded search space that it encompasses.ResultsWe evaluate our method over a gold-standard corpus from the 2008 i2b2 Obesity Challenge. This experimentation emphasizes positive results for sensitivity and specificity measures. Our new approach provides better recall with high precision of the obtained results. The most promising aspect of this approach consists in the discovery of positive results not present our Obesity NLP reference set.ConclusionsTogether with a Web graphical user interface, our FCA and SQE cooperation end up being an efficient approach for refining health outcome of interest using plain terms. We consider that this approach can be extended to support other domains such as cohort building tools.
Highlights
Electronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI)
Together with a Web graphical user interface, our Formal Concept Analysis (FCA) and Semantic Query Expansion (SQE) cooperation end up being an efficient approach for refining health outcome of interest using plain terms
The system aims for broad coverage initially and asks for feedback on the furthest matches at the highest points of the lattice so that the search space can be rapidly refined with minimal input from the user
Summary
Electronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). At the same time the user is issuing queries and confirming or denying concept matches, we display search results that highlight snippets from clinical notes matching their HOI, which provides instant feedback for the user as they refine their HOI. We have implemented these tools as REST service Web APIs. Used together, FCA and SQE make it possible to order concepts semantically and search for matches that best reflect the intension of the user, i.e., the medical concepts she wants to express through a set of proposed terms. The amount of feedback can be minimized by measuring coverage of concepts at each major branch
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