The evaluation of machine-based learning models for predicting acute postoperative pain

  • Use of machine-based learning models for prediction in medicine may hold promise. Yet, predicting postoperative pain following surgery using this approach remain biased.
  • Data from this study (n=14,263 patients) showed that the CatBoost machine-based model demonstrated bias towards age, race, area deprivation index (ADI), and insurance type. In contrast, the model demonstrated fairness in terms of sex, language, and health literacy.
  • Although overall performance in predicting acute postoperative pain was favorable, bias remained for certain attributes. According to the authors, further assessment to warrant the fairness of machine-based learning tools is necessary before advancing to the clinical implementation stage.