We present a new model of patient record search, called SemanticFind, which goes beyond traditional textual and medical synonym matches in locating patient data that a clinician would want to see rather than just what they ask for. The new model is implemented by making extensive use of the UMLS semantic network, distributional semantics, and NLP, to match query terms along several dimensions in a patient record, and the returned matches are organized accordingly. The new approach finds all clinically related concepts without the user having to ask for them. An evaluation of the accuracy of SemanticFind shows that it found twice as many relevant matches compared to those found by literal (traditional) search alone, along with very high precision and recall. These results suggest potential uses for SemanticFind in clinical practice, retrospective chart reviews, and in automatically extracting quality metrics.
By: John M. Prager, Jennifer J. Liang, Murthy V. Devarakonda
Published in: RC25627 in 2016
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