Machine learning to improve frequent emergency department use prediction: a retrospective cohort study
Open accessAbstract
Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users have complex needs and associated factors are commonly evaluated using logistic regression. However, other machine learning models, especially those exploiting the potential of large databases, have been less explored. This study aims at comparing the performance of logistic regression to four machine learning models for predicting frequent emergency department use in an adult population with chronic diseases, in the province of Quebec (Canada). This is a retrospective population-based study using medical and administrative databases from the Régie de l’assurance maladie du Québec. Two definitions were used for frequent emergency department use (outcome to predict): having at least three and five visits during a year period. Independent variables included sociodemographic characteristics, healthcare service use, and chronic diseases. We compared the performance of logistic regression with gradient boosting machine, naïve Bayes, neural networks, and random forests (binary and continuous outcome) using Area under the ROC curve, sensibility, specificity, positive predictive value, and negative predictive value. Out of 451,775 ED users, 43,151 (9.5%) and 13,676 (3.0%) were frequent users with at least three and five visits per year, respectively. Random forests with a binary outcome had the lowest performances (ROC curve: 53.8 [95% confidence interval 53.5–54.0] and 51.4 [95% confidence interval 51.1–51.8] for frequent users 3 and 5, respectively) while the other models had superior and overall similar performance. The most important variable in prediction was the number of emergency department visits in the previous year. No model outperformed the others. Innovations in algorithms may slightly refine current predictions, but access to other variables may be more helpful in the case of frequent emergency department use prediction.
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Machine learning and artificial intelligence approaches have demonstrated growing promise in diabetes risk prediction, complication detection and healthcare utilization forecasting.
“More recently, machine learning and artificial intelligence approaches have demonstrated growing promise in diabetes risk prediction, complication detection and healthcare utilization forecasting [21–28].”
From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [22]
The Introduction and Discussion support machine-learning prediction of frequent emergency-department use, a form of healthcare utilization forecasting. The Discussion > Model performance also reports a separate study predicting uncontrolled diabetes, but that is not prediction of who will develop diabetes. Our copy does not support the complication-detection part of the claim, and its Conclusions say the models showed no clear superiority over simpler models for ED-use prediction. The indexed passages provide no page numbers. (LLM, conf=0.87)
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- Authors
- Chiu, Y. M., Courteau, J., Dufour, I., Vanasse, A., & Hudon, C.
- Affiliations
- Université de Sherbrooke (CA)
- McGill University (CA)
- Corresponding author
- Chiu, Yohann M.
- Venue
- Scientific Reports
- Published
- 2023-02-03
- Volume
- 13
- Issue
- 1
- Pages
- 1981-1981
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9898278
- OpenAlex
- W4319081168
- Cited by
- 20
- References
- 65
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 4
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fonds de Recherche du Québec - Santé
- Fonds de recherche du Québec – Nature et technologies
- Centre de recherche du Centre hospitalier de l’université de Sherbrooke
- Québec SPOR SUPPORT Unit
- Topics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- emergency department visits, chronic disease, machine learning prediction models, random forest, gradient boosting, artificial neural networks, naive Bayes, logistic regression, health care utilization, hospitalization risk, Quebec
- MeSH terms
- Machine Learning, Adult, Bayes Theorem, Chronic Disease, Emergency Service, Hospital, Humans, Retrospective Studies
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