Early Prediction of Sepsis Using Machine Learning Algorithms: A Review

With a high rate of morbidity as well as mortality, sepsis is a major worldwide health concern. The condition is complex, making diagnosis difficult, and mortality is still high, especially in intensive care units (ICUs), despite treatments. In fact, the most common reason for death in ICUs is sepsis. As sepsis progresses, fatality rates rise, making prompt diagnosis essential. Rapid sepsis detection is essential for bettering patient outcomes. To help with early sepsis prediction, machine learning models have been created using electronic health records (EHRs) to address this issue. However, there is currently little use of these models in ICU clinical practice or research. The objective is to use machine learning to provide early sepsis prediction and identification utilizing cutting-edge algorithms. For adult patients in particular, the creation of very precise algorithms for early sepsis prediction is crucial. Advanced analytical and machine learning approaches have the potential to predict patient outcomes and improve the automation of clinical decision-making systems when used in Electronic Health Records. The machine learning methods created for early sepsis prediction are briefly described in this paper.