3 citations · 9 across the 13 of their papers we have counts for
5 papers · 1 filter
Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database
Negin Ashrafi, Armin Abdollahi, Jiahong Zhang +1
Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between…
Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques
Negin Ashrafi, Armin Abdollahi, Maryam Pishgar
Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patient…
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
Arseniy Shumilov, Yueting Zhu, Negin Ashrafi +4
Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Prev…
Effect of a Process Mining based Pre-processing Step in Prediction of the Critical Health Outcomes
Negin Ashrafi, Armin Abdollahi, Greg Placencia +1
Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences…
Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data
Armin Abdollahi, Negin Ashrafi, Maryam Pishgar
Background: Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic stroke…