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Intraoperative Ultrasound in Spinal Surgery
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Surgical classification using natural language processing of informed consent forms in spine surgery.

Michael D Shost1,2, Seth M Meade1,2,3, Michael P Steinmetz2,3

  • 11Case Western Reserve University, School of Medicine.

Neurosurgical Focus
|June 7, 2023
PubMed
Summary

This study developed a Natural Language Processing (NLP) classifier to automatically categorize spine surgeries from patient consent forms, achieving 91% accuracy. This tool enhances research efficiency by quickly classifying surgical data for analysis and insights.

Keywords:
classificationcurriculum developmentmachine learningnatural language processing

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Area of Science:

  • Spine Surgery
  • Medical Informatics
  • Machine Learning

Background:

  • Manual classification of surgical forms is time-consuming in clinical spine surgery research.
  • Natural Language Processing (NLP) offers automated text analysis capabilities.
  • Developing an NLP tool can streamline the categorization of patient surgical characteristics.

Purpose of the Study:

  • To design and evaluate an NLP classifier for automatically categorizing patients based on surgical procedures from consent forms.
  • To improve the efficiency and accuracy of surgical data classification for research.

Main Methods:

  • Trained an NLP classifier on 12,239 spine surgery consent forms, labeled by Current Procedural Terminology (CPT) codes.
  • Classified 7 common spine surgeries, splitting the data into 80% training and 20% testing sets.
  • Evaluated classifier performance using CPT codes to determine accuracy metrics.

Main Results:

  • The NLP surgical classifier achieved an overall weighted accuracy of 91%.
  • Positive predictive value (PPV) was highest for Anterior Cervical Discectomy and Fusion (96.8%) and lowest for Lumbar Microdiscectomy (85.0%).
  • Sensitivity was highest for Lumbar Laminectomy and Fusion (96.7%); Negative predictive value and specificity exceeded 95% for all categories.

Conclusions:

  • NLP text classification significantly enhances the efficiency of classifying surgical procedures for research.
  • This automated classification benefits institutions with limited data review resources and aids trainees and surgeons in tracking experience.
  • Accurate and rapid surgical type recognition facilitates extracting new insights correlating interventions with patient outcomes.