A Clustering-Based Machine Learning Approach for Mortality Prediction in Gastrointestinal Bleeding: Development and Validation.
Publication/Presentation Date
1-1-2026
Abstract
BACKGROUND AND AIMS: Gastrointestinal bleeding (GIB) is a life-threatening emergency with considerable morbidity and mortality. Traditional risk scores like AIMS65 and Glasgow-Blatchford Score (GBS) are limited in capturing nonlinear clinical interactions. We developed and externally validated a machine learning model to predict 30-day mortality in GIB patients.
METHODS: We retrospectively analyzed 5453 emergency department patients with GIB from the Medical Information Mart for Intensive Care IV-Emergency Department database for model development, with external validation using 7166 patients from Jefferson Health. Sixteen clinical and laboratory variables were selected based on a literature review and clinical relevance. The development cohort was divided into training (80%) and internal validation (20%) sets. Survivors were partitioned into 24 clusters using K-means, with separate random forest models trained on each cluster, combined with all deceased cases. Performance was evaluated using the area under the receiver-operating characteristic curve, sensitivity, and specificity on the external validation set, then benchmarked against AIMS65 and the GBS.
RESULTS: The model achieved an area under the receiver-operating characteristic curve of 0.884 (95% confidence interval: 0.863-0.905) on internal validation and 0.882 (95% confidence interval: 0.863-0.900) on external validation, significantly outperforming AIMS65 (0.737) and GBS (0.768) (
CONCLUSION: Our model provides superior risk stratification for 30-day mortality in GIB compared to conventional scores, with validated generalizability and potential for integration into electronic health record systems.
Volume
5
Issue
7
First Page
100985
Last Page
100985
ISSN
2772-5723
Published In/Presented At
Alomari, L., Al-Fakhouri, Z., Jaradat, J., Simadibrata, D., Al-Riyalat, A., Lam, J., Otabor, E., Jarrar, Y., Massad, A., Alomari, J., Abdel-Jalil, A., & Ezaz, G. (2026). A Clustering-Based Machine Learning Approach for Mortality Prediction in Gastrointestinal Bleeding: Development and Validation. Gastro hep advances, 5(7), 100985. https://doi.org/10.1016/j.gastha.2026.100985
Disciplines
Medicine and Health Sciences
PubMedID
42205168
Department(s)
Fellows and Residents
Document Type
Article