What does this research mean for the field?

Machine learning models can predict early postoperative nausea and vomiting (PONV) with moderate accuracy using routinely available non-invasive clinical indicators. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to assess the feasibility of using machine learning methods to predict early postoperative nausea and vomiting risk based on routine clinical indicators.

February 27, 2026Open Access

Explainable Machine Learning for Prediction of Early Postoperative Nausea and Vomiting After General Anesthesia

Key Points

This study aims to assess the feasibility of using machine learning methods to predict early postoperative nausea and vomiting risk based on routine clinical indicators.
Utilized a retrospective dataset from 927 patients containing 16 non-invasive clinical indicators.
Evaluated various machine learning models, including random forest, logistic regression, and deep learning architectures.
Applied explainable artificial intelligence techniques to identify influential predictors of postoperative nausea and vomiting.
Random forest model achieved an accuracy of 83.5% with balanced precision of 80.81% and recall of 83.5%.
Logistic regression model obtained an AUC of 0.6905.
Identified seven key predictors, including pharmacologic interventions and demographic factors, with dexamethasone showing a negative association with PONV risk.

Abstract

Purpose: Postoperative nausea and vomiting (PONV) remains one of the most common adverse effects associated with anesthesia care. This study aimed to explore the feasibility of applying machine learning models trained exclusively on routinely available non-invasive clinical indicators to predict early PONV risk. Explainable artificial intelligence techniques were also employed to identify the most influential predictors of early PONV. Patients and Methods: A retrospective dataset from Cathay General Hospital, including 927 patient cases and 16 non-invasive clinical indicators, was used to investigate early PONV risk prediction. This study evaluated the predictive performance of several traditional machine learning models, deep learning architectures, and ensemble learning methods to compare their classification capabilities. Results: Overall, the models demonstrated moderate discriminative performance. The random forest model achieved an accuracy of 83.5% with balanced precision (80.81%) and recall (83.5%), while the logistic regression model attained an AUC of 0.6905. Analysis of positive SHAP values identified the top 7 most influential predictors of early PONV. These included pharmacologic interventions (eg, neostigmine), pre-existing comorbidities (eg, history of nausea and vomiting, history of cardiovascular disease), demographic characteristics (eg, gender), postoperative pain, and anesthetic and surgical factors (eg, type of surgery and duration of anesthesia). Moreover, SHAP analysis revealed that the use of dexamethasone was negatively associated with the predicted risk in the model, suggesting its potential protective role in the prevention of early PONV. Conclusion: By generating explainable outputs, this study bridges the gap between algorithmic prediction and clinical decision-making, allowing anesthesiologists to better recognize underlying risk factors and make informed, evidence-based decisions in perioperative management. Keywords: postoperative nausea and vomiting, machine learning, explainable artificial intelligence, risk prediction, anesthesiology

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