Literature DB >> 34795088

Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form.

Melissa P Resnick1, Frank LeHouillier1, Steven H Brown2, Keith E Campbell2, Diane Montella2, Peter L Elkin1,3,4,3.   

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.

Entities:  

Keywords:  Clinical Decision Support; Controlled Terminology; Interoperability; Natural Language Processing

Mesh:

Year:  2021        PMID: 34795088      PMCID: PMC9088023          DOI: 10.3233/SHTI210822

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  4 in total

1.  Biomedical ontologies in action: role in knowledge management, data integration and decision support.

Authors:  O Bodenreider
Journal:  Yearb Med Inform       Date:  2008

2.  HTP-NLP: A New NLP System for High Throughput Phenotyping.

Authors:  Daniel R Schlegel; Chris Crowner; Frank Lehoullier; Peter L Elkin
Journal:  Stud Health Technol Inform       Date:  2017

3.  Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes.

Authors:  Peter L Elkin; David A Froehling; Dietlind L Wahner-Roedler; Steven H Brown; Kent R Bailey
Journal:  Ann Intern Med       Date:  2012-01-03       Impact factor: 25.391

4.  Automated identification of postoperative complications within an electronic medical record using natural language processing.

Authors:  Harvey J Murff; Fern FitzHenry; Michael E Matheny; Nancy Gentry; Kristen L Kotter; Kimberly Crimin; Robert S Dittus; Amy K Rosen; Peter L Elkin; Steven H Brown; Theodore Speroff
Journal:  JAMA       Date:  2011-08-24       Impact factor: 56.272

  4 in total

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