Early Detection of Sepsis in Adult ICU Patients Using Machine Learning Techniques at a University Medical College Hospital

Sepsis remains a significant public health issue associated with high mortality, morbidity, and related health costs.Although numerous studies are conducted every year on how to reduce the mortality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early and accurate diagnosis is crucial for effective treatment and improved outcomes. However, the complexity and diversity of sepsis-related complications present obstacles in the diagnostic process. Therefore, our primary goal is to develop a multicentric Machine learning (ML) model that can predict sepsis early before its medically confirmed onset.

It can be expected that the development of an accurate predictive model can reduce the severity of sepsis risk at an early stage alert, assist clinicians in decision-making, enhance patient outcomes, and reduce mortality rates through timely and accurate intervention.