ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2016
Predicting Emergency Department Visits.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Transport-based transfer learning on Electronic Health Records: application to detection of treatment disparities.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.BMC emergency medicine · 2024Article
- Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?BMC health services research · 2022Article
- Can the adverse childhood experiences (ACEs) checklist be utilized to predict emergency department visits among children and adolescents?BMC medical research methodology · 2021Article
- Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States.JMIR medical informatics · 2021Article
- Management of Inflammatory Bowel Disease Patients With Clinical Care Pathways Reduces Emergency Department Utilization.Crohn's & colitis 360 · 2020Article
- The Role of Caregiver-Reported Risks in Predicting Adverse Pediatric Outcomes.Journal of pediatric psychology · 2020Article
- Application of Machine Learning Techniques to Analyze Patient Returns to the Emergency Department.Journal of personalized medicine · 2020Article
- Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial.Ethnicity & disease · 2020Review
- Predicting 72-hour and 9-day return to the emergency department using machine learning.JAMIA open · 2019Article
- Towards a Learning Health System to Reduce Emergency Department Visits at a Population Level.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High utilizers of emergency departments account for a disproportionate number of visits, often for nonemergency conditions. This study aims to identify these high users prospectively. Routinely recorded registration data from the Indiana Public Health Emergency Surveillance System was used to predict whether patients would revisit the Emergency Department within one month, three months, and six months of an index visit. Separate models were trained for each outcome period, and several predictive models were tested. Random Forest models had good performance and calibration for all outcome periods, with area under the receiver operating characteristic curve of at least 0.96. This high performance was found to be due to non-linear interactions among variables in the data. The ability to predict repeat emergency visits may provide an opportunity to establish, prioritize, and target interventions to ensure that patients have access to the care they require outside an emergency department setting.
Identifiers
27570684PMC5001776What OpenQuestion holds
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.