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Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form
Melissa P. Resnick, Frank LeHouillier, Steven H. Brown, Keith E. Campbell, Diane Montella, Peter L. Elkin
89 - 93
10.3233/SHTI210822
Research Article
Studies in Health Technology and Informatics
Volume 287: Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era
Abstract

Objective:

One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). Standards, such as terminologies (findability), assist with important tasks like interoperability, Natural Language Processing (NLP) (accessibility) and decision support (reusability). One terminology, Solor, integrates SNOMED CT, LOINC and RxNorm. We describe Solor, HL7 Analysis Normal Form (ANF), and their use with the high definition natural language processing (HD-NLP) program.

Methods:

We used HD-NLP to process 694 clinical narratives prior modeled by human experts into Solor and ANF. We compared HD-NLP output to the expert gold standard for 20% of the sample. Each clinical statement was judged “correct” if HD-NLP output matched ANF structure and Solor concepts, or “incorrect” if any ANF structure or Solor concepts were missing or incorrect. Judgements were summed to give totals for “correct” and “incorrect”.

Results:

113 (80.7%) correct, 26 (18.6%) incorrect, and 1 error. Inter-rater reliability was 97.5% with Cohen’s kappa of 0.948.

Conclusion:

The HD-NLP software provides useable complex standards-based representations for important clinical statements designed to drive CDS.

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