Around the world, sepsis is a primary cause of death. The immune system's response to an infection causes sepsis, a life-threatening illness. In ICUs, this illness is very common, and it can be dangerous for patients at times. It has a mortality rate of 50%. In recent years, machine learning algorithms have received a lot of attention for their ability to predict clinically significant events. The argument over sepsis definition and its implications on the construction of prediction models; input feature selection and availability; model performance, output, and clinical utility are all discussed briefly in this work. In the long run, a rigorous multidisciplinary approach to improving our understanding of the application of machine learning techniques for the early diagnosis of sepsis may show promise in aiding medical decision-making when dealing with this heterogeneous and complicated disorder.
₹10000 (INR)
NON IEEE -2022