Ozaydin, Bunyamin

An artificial intelligence-based framework for predicting emergency department overcrowding: Development and evaluation study.

Citation
Vural O, Ozaydin B, Aram KY, Booth J, Lindsey BF, Ahmed A. An artificial intelligence-based framework for predicting emergency department overcrowding: Development and evaluation study. JMIR Med Inform. 2025 Sep 17;13:e73960. doi: 10.2196/73960. PMID: 40961493; PMCID: PMC12489414.
Principal Investigator
Ahmed, Abdulaziz
Ozaydin, Bunyamin
Project Name
Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Assessing the impact of external and internal factors on emergency department overcrowding.

Citation
Ahmed A, Aram KY, Alzeen M, Vural O, Booth J, Lindsey BF, Ozaydin B. Assessing the impact of external and internal factors on emergency department overcrowding. Healthcare (Basel). 2025 Oct 14;13(20):2577. doi: 10.3390/healthcare13202577. PMID: 41154256; PMCID: PMC12562336.
Principal Investigator
Ahmed, Abdulaziz
Ozaydin, Bunyamin
Project Name
Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

Citation
Lee SJ, Alzeen M, Ahmed A. Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing. J Am Med Inform Assoc. 2024 Apr 3;31(4):958-967. doi: 10.1093/jamia/ocae018. PMID: 38349846; PMCID: PMC10990499.
Principal Investigator
Ahmed, Abdulaziz
Ozaydin, Bunyamin
Project Name
Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Description

This research will use deep learning models to move a reactive full capacity protocol (FCP) for emergency department (ED) overcrowding interventions into a proactive FCP by predicting patient flow measures so that interventions may be activated to avoid ED overcrowding.

Grant Number
R21 HS029410
Principal Investigator(s)
Ahmed, Abdulaziz
Ozaydin, Bunyamin