Nowadays, across the most important problems faced by health centers are those caused by
the existence of patients who do not attend their appointments. Among others, these patients …

Background No-show to medical appointments has significant adverse effects on healthcare
systems and their clients. Using machine learning to predict no-shows allows managers to …

Background: The phenomenon of patients missing booked appointments without canceling
them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA) …

Background No-show appointments pose a significant challenge for healthcare providers,
particularly in rural areas. In this study, we developed an evidence-based predictive model …

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US)
appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover …

This study aims to develop an accurate machine learning model for predicting no-shows in
pediatric outpatient clinics at King Faisal Specialist Hospital and Research Centre …

Background Pandemics such as the COVID-19 pandemic and other severe health care
disruptions endanger individuals to miss essential care. Machine learning models that …

Background Primary health care (PHC) services face operational challenges due to high
patient volumes, leading to complex management needs. Patients access services through …

Purpose Preanesthesia screening is critical to evaluate the patient's medical and surgical
history before any procedure or surgery to assess for risks and to optimize outcomes during …

Background Analyzing patients' nonattendance at medical appointments helps address an
issue impacting the management and sustainability of health systems globally, providing …