ArticleDiagnostics (Basel, Switzerland)2021
A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Assessment of Intraoperative Scoring Systems for Predicting Cytoreduction Outcome in Peritoneal Metastatic Disease: A Systematic Review and Meta-analysis.Annals of surgical oncology · 2024Pooled it
- Assessment of Intraoperative Scoring Systems for Predicting Cytoreduction Outcome in Peritoneal Metastatic Disease: A Systematic Review and Meta-analysis.Annals of surgical oncology · 2024Pooled it
- Evaluation of a Generative Medical Event Foundation Model for Predicting Post-Discharge Trajectories in Emergency Department Abdominal Pain.medRxiv : the preprint server for health sciences · 2026Article
- Vaginal Probiotic Potential of the Nomadic Species Limosilactobacacillus fermentum Under Dysbiotic Vaginal Conditions: Population Genomic Insights and Anti-Pathogenic Efficacy of a Vaginal Isolate VL9.Probiotics and antimicrobial proteins · 2026Article
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Unsupervised Clustering of 41,728 Emergency Department Visits: Insights into Patient Profiles and KTAS Reliability.Healthcare (Basel, Switzerland) · 2025Article
- Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits.Frontiers in public health · 2025Article
- Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.BMJ health & care informatics · 2024Article
- Prediction Models Using Decision Tree and Logistic Regression Method for Predicting Hospital Revisits in Peritoneal Dialysis Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.BMC medical informatics and decision making · 2024Article
- Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.BMC emergency medicine · 2024Article
- Activation of the adenosine A1 receptor in the lumbosacral spinal cord improves bladder overactivity in rats with cystitis induced by cyclophosphamide.International urology and nephrology · 2023Article
- Construction of a nomogram model for predicting peritoneal metastasis in gastric cancer: focused on cardiophrenic angle lymph node features.Abdominal radiology (New York) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69-0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69-0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.
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