ArticleBMC medical research methodology2023
Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06445920 (Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery Based on LASSO and Stepwise Regression), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery Based on LASSO and Stepwise Regression
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Effect of stellate ganglion block on postoperative nausea and vomiting after general anesthesia: A meta-analysis.The Journal of international medical research · 2025Pooled it
- Construction of performance score dynamic prediction system for clinical departments using explainable machine learning.Health information science and systems · 2026Article
- Review
- Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.JMIR medical informatics · 2026Article
- Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.Scientific reports · 2026Article
- 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
- Enhancing clinical training evaluation: leveraging artificial intelligence algorithms for effective online practicum assessment.BMC medical education · 2026Article
- Explainable Machine Learning for Prediction of Early Postoperative Nausea and Vomiting After General Anesthesia.Journal of multidisciplinary healthcare · 2026Article
- Performance comparison of different machine learning models to predict the risk of postcraniotomy nausea and vomiting: a retrospective study.Frontiers in medicine · 2026Article
- Postoperative nausea and vomiting: translating pathophysiological mechanisms into clinical management.Perioperative medicine (London, England) · 2025Review
- Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.Annals of medicine · 2025Article
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Article
- Application of Machine Learning to Predict Postoperative Nausea and Vomiting in Laparoscopic Sleeve Gastrectomy.Obesity surgery · 2025Article
- The neural mechanism and pathways underlying postoperative nausea and vomiting: a comprehensive review.European journal of medical research · 2025Review
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- Investigation of the attitudes and behaviors of anesthesiologists regarding the prophylaxis and treatment of postoperative nausea and vomiting.SAGE open medicine · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePONV reduces patient satisfaction and increases hospital costs as patients remain in the hospital for longer durations. In this study, we build a preliminary artificial intelligence algorithm model to predict early PONV in patients.
methodsWe use R for statistical analysis and Python for the machine learning prediction model.
resultsAverage characteristic engineering results showed that haloperidol, sex, age, history of smoking, and history of PONV were the first 5 contributing factors in the occurrence of early PONV. Test group results for artificial intelligence prediction of early PONV: in terms of accuracy, the four best algorithms were CNNRNN (0.872), Decision Tree (0.868), SVC (0.866) and adab (0.865); in terms of precision, the three best algorithms were CNNRNN (1.000), adab (0.400) and adab (0.868); in terms of AUC, the top three algorithms were Logistic Regression (0.732), SVC (0.731) and adab (0.722). Finally, we built a website to predict early PONV online using the Streamlit app on the following website: ( https://zhouchengmao-streamlit-app-lsvc-ad-st-app-lsvc-adab-ponv-m9ynsb.streamlit.app/ ).
conclusionArtificial intelligence algorithms can predict early PONV, whereas logistic regression, SVC and adab were the top three artificial intelligence algorithms in overall performance. Haloperidol, sex, age, smoking history, and PONV history were the first 5 contributing factors associated with early PONV.
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Registered trials
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.