ArticleJournal of medical systems2025
Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Establishment and validation of a machine learning-based prediction model for sepsis-induced coagulopathy.Journal of thrombosis and thrombolysis · 2026Article
- Article
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Mass gathering events underscore serious vulnerabilities in health care capacity in Canada.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2026Article
- AI-powered models for overcrowding prediction at TUMS hospitals.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with its detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective time series data such as weather, availability of hospital beds, calendar variables and occupancy statistics from a large Nordic ED with a LightGBM model. We predict mortality associated crowding for the whole ED and individually for its different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using time series data is feasible.
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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.