ArticleScientific reports2023
Development and assessment of novel machine learning models to predict the probability of postoperative nausea and vomiting for patient-controlled analgesia.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Risk factors for postoperative nausea and vomiting after general anesthesia: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Prevalence and associated factors of intraoperative Nausea and Vomiting of mothers who gave birth with cesarean section under regional anesthesia: a systematic review and meta-analysis; 2023.BMC pregnancy and childbirth · 2025Pooled it
- Review
- DSPONVNet: a multimodal deep learning model integrating intraoperative monitoring and clinical features for predicting postoperative nausea and vomiting risk.BMC medical research methodology · 2026Article
- Application of machine learning for the prediction of post-operative nausea and vomiting in adult surgical patients - A systematic review.Indian journal of anaesthesia · 2026Article
- A dynamic nomogram for predicting postoperative nausea and vomiting after laparoscopic surgery: a prospective study.BMC anesthesiology · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Postoperative nausea and vomiting: current concepts, management strategies, and future perspectives.Frontiers in pharmacology · 2026Review
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Application of Machine Learning to Predict Postoperative Nausea and Vomiting in Laparoscopic Sleeve Gastrectomy.Obesity surgery · 2025Article
- Investigation of the attitudes and behaviors of anesthesiologists regarding the prophylaxis and treatment of postoperative nausea and vomiting.SAGE open medicine · 2025Article
- Factors associated with acute clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.Frontiers in medicine · 2025Article
- The anesthesiologist's guide to critically assessing machine learning research: a narrative review.BMC anesthesiology · 2024Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postoperative nausea and vomiting (PONV) can lead to various postoperative complications. The risk assessment model of PONV is helpful in guiding treatment and reducing the incidence of PONV, whereas the published models of PONV do not have a high accuracy rate. This study aimed to collect data from patients in Sichuan Provincial People's Hospital to develop models for predicting PONV based on machine learning algorithms, and to evaluate the predictive performance of the models using the area under the receiver characteristic curve (AUC), accuracy, precision, recall rate, F1 value and area under the precision-recall curve (AUPRC). The AUC (0.947) of our best machine learning model was significantly higher than that of the past models. The best of these models was used for external validation on patients from Chengdu First People's Hospital, and the AUC was 0.821. The contributions of variables were also interpreted using SHapley Additive ExPlanation (SHAP). A history of motion sickness and/or PONV, sex, weight, history of surgery, infusion volume, intraoperative urine volume, age, BMI, height, and PCA_3.0 were the top ten most important variables for the model. The machine learning models of PONV provided a good preoperative prediction of PONV for intravenous patient-controlled analgesia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.