Predicting the risk of hospital readmissions using a machine learning approach: a case study on patients undergoing skin procedures.

Introduction: Even with modern advancements in medical care, one of the persistent challenges hospitals face is the frequent readmission of patients. These recurrent admissions not only escalate healthcare expenses but also amplify mental and emotional strain on patients.

Methods: This research delved into two primary areas: unraveling the pivotal factors causing the readmissions, specifically targeting patients who underwent dermatological treatments, and determining the optimal machine learning algorithms that can foresee potential readmissions with higher accuracy.

Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia, urinary tract infection, and other illnesses. Some of its symptoms are fever, tachycardia, tachypnea, etc.

Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions.

This study presented a qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Accordingly, an exploratory and applied research design was employed in this study. The research sample included 15 experienced staff working in main and related positions in Neyr Perse Company.