Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models
Clinical and Experimental Emergency Medicine. 2020;7(3):197-205 
PREDICTING THE MORTALITY OF PNEUMONIA PATIENTS VISITING THE EMERGENCY DEMARTMENT THROUGH MACHINE LEARNING
Respirology. 2017;22:273-273
Predicting 30-day mortality for patients with acute heart failure in the emergency department
The Journal of Emergency Medicine. 2018;54(2):267-268
Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learning
BMC Emergency Medicine. 2021;21(1):
Explainable Machine Learning Models for Pneumonia Mortality Risk Prediction Using MIMIC-III Data
2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI). 2022;
Predicting 90-day readmission for patients with heart failure: a machine learning approach using XGBoost
. 2022;
152 Pulmonary Embolism in Patients With Cancer: Predicting 30-day Mortality
Annals of Emergency Medicine. 2022;80(4):S70
Sepsis patient evaluation emergency department (SPEED) score & mortality in emergency department sepsis (MEDS) score in predicting 28-day mortality of emergency sepsis patients
Chinese Journal of Traumatology. 2019;22(6):316-322
Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients
Surgical Oncology. 2022;44:101810
Predicting 72-hour and 9-day return to the emergency department using machine learning
JAMIA Open. 2019;2(3):346-352