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Related Experiment Video

Updated: Jul 28, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.

Cheng-Mao Zhou1,2, Ying Wang3, Qiong Xue3

  • 1Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China. zhouchengmao187@foxmail.com.

BMC Medical Research Methodology
|May 31, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an artificial intelligence model to predict postoperative nausea and vomiting (PONV), identifying key risk factors like haloperidol and patient history. The AI model offers a promising tool for early PONV prediction.

Keywords:
AUCDeep learningMachine learningPONVSVC

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Prediction Models

Background:

  • Postoperative nausea and vomiting (PONV) significantly impacts patient satisfaction and increases healthcare costs due to extended hospital stays.
  • Early identification and management of PONV are crucial for improving patient outcomes and optimizing resource utilization.

Purpose of the Study:

  • To develop and evaluate a preliminary artificial intelligence (AI) algorithm model for the early prediction of PONV.
  • To identify key clinical factors contributing to the occurrence of early PONV.

Main Methods:

  • Utilized R for statistical analysis and Python for developing the machine learning prediction model.
  • Engineered features to identify the top contributing factors for early PONV.
  • Evaluated multiple AI algorithms including CNNRNN, Decision Tree, SVC, and adaboost for prediction accuracy, precision, and AUC.

Main Results:

  • Haloperidol administration, patient sex, age, smoking history, and previous PONV history were identified as the top 5 predictors of early PONV.
  • The CNNRNN algorithm demonstrated the highest accuracy (0.872), while CNNRNN also showed the highest precision (1.000).
  • Logistic Regression, SVC, and adaboost achieved the top AUC scores, indicating strong predictive performance.

Conclusions:

  • AI algorithms are capable of accurately predicting early PONV.
  • Logistic Regression, SVC, and adaboost algorithms exhibited the best overall performance in predicting PONV.
  • Established a publicly accessible online tool for predicting early PONV using a Streamlit app.