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Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language

Melissa N Poulsen1, Philip J Freda2, Vanessa Troiani3

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Summary

A new annotation schema helps identify opioid use disorder (OUD) severity in clinical notes, improving natural language processing (NLP) tools for better patient care and treatment.

Keywords:
adultadultsannotationannotation schemaclinical notesmental healthnatural language processingopioidopioid related disordersopioid use disorderseverity scoresubstance misusesubstance use disorders

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Area of Science:

  • Medical Informatics
  • Clinical Natural Language Processing
  • Substance Use Disorders

Background:

  • Accurate assessment of opioid use disorder (OUD) status and severity is crucial for effective patient management.
  • Clinical notes contain vital information for identifying and characterizing OUD, necessitating advanced natural language processing (NLP) tools.
  • Development of NLP tools requires reliably labeled OUD-relevant text and an understanding of clinical documentation patterns.

Purpose of the Study:

  • To develop and evaluate an annotation schema for characterizing OUD and its severity.
  • To document patterns of OUD-relevant information within clinical notes from diverse patient cohorts.
  • To inform the development of automated NLP methods for OUD detection and characterization.

Main Methods:

  • Developed an annotation schema based on DSM-5 criteria for OUD severity.
  • Annotated 1436 sentences from 100 adult patients' clinical notes at the sentence level.
  • Calculated OUD severity scores using 27 classes and determined positive predictive values for OUD severity detection.

Main Results:

  • The annotation schema demonstrated good interannotator agreement (>70% for 11/15 batches).
  • The mean OUD severity score for non-control patients was 5.1 (SD 3.2), indicating moderate OUD.
  • The positive predictive value for detecting moderate or severe OUD was 0.71, with progress notes and ED/outpatient settings yielding the most information.

Conclusions:

  • The annotation schema shows strong potential for inferring OUD severity from clinical notes.
  • This schema facilitates NLP tool development for improved OUD prevention, diagnosis, and treatment.
  • Identifying documentation patterns aids in refining NLP approaches for OUD management.