ArticleJAMIA open2019
Predicting 72-hour and 9-day return to the emergency department using machine learning.
Article in JAMIA open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.Journal of medical systems · 2025Pooled it
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- Observational
- Factors associated with overall and high-risk return visits to the emergency department: a vital sign trajectory approach.BMC emergency medicine · 2025Article
- Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits.Frontiers in public health · 2025Article
- Seizing the Teachable Moment: A Pilot Study of Preventive Lifestyle Education in the Emergency Department.Cureus · 2024Article
- Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.PLOS digital health · 2024Article
- Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.BMJ health & care informatics · 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
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions.Decision support systems · 2022Article
- Artificial intelligence decision points in an emergency department.Clinical and experimental emergency medicine · 2022Article
- Patterns in emergency department unscheduled return visits during the COVID-19 pandemic.The American journal of emergency medicine · 2022Article
- A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.Diagnostics (Basel, Switzerland) · 2021Article
- Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study.Journal of the American College of Emergency Physicians open · 2021Article
- Impact of diagnostic errors on adverse outcomes: learning from emergency department revisits with repeat CT or MRI.Insights into imaging · 2021Article
- Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.Scientific reports · 2021Article
- Developing machine learning models to personalize care levels among emergency room patients for hospital admission.Journal of the American Medical Informatics Association : JAMIA · 2021Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record.
methodsThis retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.
resultsA total of 330 631 discharges were available for analysis, with 29 058 discharges (8.8%) resulting in 72-h return and 52 748 discharges (16.0%) resulting in 9-day return to either ED. LR models using administrative data yielded test AUCs of 0.69 (95% confidence interval [CI] 0.68-0.70) and 0.71(95% CI 0.70-0.72), while gradient boosting models using administrative data yielded test AUCs of 0.73 (95% CI 0.72-0.74) and 0.74 (95% CI 0.73-0.74) for 72-h and 9-day return, respectively. Gradient boosting models using variables available at triage yielded test AUCs of 0.75 (95% CI 0.74-0.76) and 0.75 (95% CI 0.74-0.75), while those using the full set of variables yielded test AUCs of 0.76 (95% CI 0.75-0.77) and 0.75 (95% CI 0.75-0.76). Reduced models using the top 20 variables yielded test AUCs of 0.73 (95% CI 0.71-0.74) and 0.73 (95% CI 0.72-0.74). DISCUSSION AND
conclusionGradient boosting models leveraging clinical data are superior to LR models built on administrative data at predicting 72-h and 9-day returns.
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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.