ArticleFrontiers in medicine2024
Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Consensus on the diagnosis and treatment of non-traumatic acute abdomen in elderly patients.World journal of emergency medicine · 2026Article
- Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.Abdominal radiology (New York) · 2026Article
- Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models.Frontiers in surgery · 2026Review
- Deep learning enables accurate diagnosis of acute cholecystitis and prediction of suppuration using noncontrast CT.iScience · 2025Article
- Review
- Development and validation of risk prediction models for high-risk patients with non-traumatic acute abdominal pain: a prospective observational study.Frontiers in public health · 2025Observational
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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Authors and funding
10 authors.
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Abstract
Background: Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources. Methods: In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model. Results: Eleven clinical indicators with statistical significance ( Conclusion: The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.
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