J R Campbell1. 1. Department of Internal Medicine, University of Nebraska Medical Center, Nebraska Health Systems; Omaha, NE 68198-3331, USA. Campbell@unmc.edu
Abstract
OBJECTIVE: To evaluate the utility of SNOMED RT in support of a natural language interface for encoding of clinical assessments. METHOD: Using a random sample of clinical terms from the UNMC Lexicon, I mapped the terminology into canonical data entries using SNOMED RT. Working from the source term language, I evaluated lexical mapping to the SNOMED term set, and the function of the SNOMED RT semantic network in support of a language-based clinical coding interface. RESULTS: Ambiguity in the source terms was low at 0.3%. Lexical (language-based) mapping could account for only 48.8% of meaning from the source terms. The RT semantic network accounted for 39.5% of meaning, and supplementing the lexical map this led to 80.2% capture of source content. Error rates in the segment of RT which I reviewed were low at 0.6%. 97.6% of source content could be accurately captured in SNOMED RT. CONCLUSION: SNOMED RT supported an accurate and reliable representation of clinical assessment data in this sample. The semantic network of RT substantially enhanced the encoding of concepts relative to lexical mapping. However these data suggest that natural language encoding with SNOMED RT in an enterprise environment is unlikely at this time.
OBJECTIVE: To evaluate the utility of SNOMED RT in support of a natural language interface for encoding of clinical assessments. METHOD: Using a random sample of clinical terms from the UNMC Lexicon, I mapped the terminology into canonical data entries using SNOMED RT. Working from the source term language, I evaluated lexical mapping to the SNOMED term set, and the function of the SNOMED RT semantic network in support of a language-based clinical coding interface. RESULTS: Ambiguity in the source terms was low at 0.3%. Lexical (language-based) mapping could account for only 48.8% of meaning from the source terms. The RT semantic network accounted for 39.5% of meaning, and supplementing the lexical map this led to 80.2% capture of source content. Error rates in the segment of RT which I reviewed were low at 0.6%. 97.6% of source content could be accurately captured in SNOMED RT. CONCLUSION: SNOMED RT supported an accurate and reliable representation of clinical assessment data in this sample. The semantic network of RT substantially enhanced the encoding of concepts relative to lexical mapping. However these data suggest that natural language encoding with SNOMED RT in an enterprise environment is unlikely at this time.
Authors: S Trent Rosenbloom; Randolph A Miller; Kevin B Johnson; Peter L Elkin; Steven H Brown Journal: J Am Med Inform Assoc Date: 2006-02-24 Impact factor: 4.497
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Authors: S Trent Rosenbloom; Steven H Brown; David Froehling; Brent A Bauer; Dietlind L Wahner-Roedler; William M Gregg; Peter L Elkin Journal: J Am Med Inform Assoc Date: 2008-10-24 Impact factor: 4.497
Authors: S Trent Rosenbloom; Randolph A Miller; Kevin B Johnson; Peter L Elkin; Steven H Brown Journal: J Am Med Inform Assoc Date: 2007-10-18 Impact factor: 4.497