BACKGROUND: Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compare its perfo...
BACKGROUND: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. W...
BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provid...
BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algor...
BACKGROUND: Major incidents are complex, dynamic and bewildering task environments characterised by simultaneous, rapidly changing events, uncertainty and ill-structured problems. Efficient management, communication, decision-making and allocation of...