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Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine

Anthony D McDonald1, Nilesh Ade1, S Camille Peres1

  • 1Texas A&M University, USA.

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Machine learning accurately predicts procedure performance using operator and text features. This approach can enhance safety and efficiency in high-risk industries by guiding procedure design.

Keywords:
decision treemachine learningoperator performanceprocedure designrandom forest

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Area of Science:

  • Engineering
  • Human Factors
  • Computer Science

Background:

  • Procedures are critical for safety in high-risk industries.
  • Current guidelines lack quantitative analysis for diverse influencing factors.
  • Subjective assessments limit the integration of performance-influencing variables.

Purpose of the Study:

  • To assess machine learning models for predicting procedure performance.
  • To identify key operator and procedure characteristics influencing performance.
  • To develop data-driven approaches for procedure design.

Main Methods:

  • Utilized a 25-participant, four-procedure oil extraction simulation.
  • Developed and compared logistic regression (LR), random forest (RF), and decision tree (DT) algorithms.
  • Employed Boruta feature selection and 10-fold cross-validation for model optimization.

Main Results:

  • RF, DT, and LR models achieved AUCs of 0.78, 0.77, and 0.75, respectively.
  • Models significantly outperformed LR with operator features only (AUC 0.61).
  • Key predictors included operator experience, familiarity, and text-based metrics (word/character counts).

Conclusions:

  • Machine learning offers a promising method for predicting step-level procedure performance.
  • Models can guide procedure design, but require further validation.
  • Text characteristics like brevity correlate with correct step execution.