Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D., Buchman, T.G.: An interpretable machine learning model for accurate prediction of sepsis in the ICU. McCoy, A., Das, R.: Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. Henry, K.E., Hager, D.N., Pronovost, P.J., Saria, S.: A targeted real-time early warning score (TREWScore) for septic shock. Lambden, S., Laterre, P.F., Levy, M.M., Francois, B.: The SOFA score-development, utility and challenges of accurate assessment in clinical trials. Seymour, C.W., et al.: Time to treatment and mortality during mandated emergency care for sepsis. Rudd, K.E., et al.: Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of disease study. Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (Sepsis-3).

These findings demonstrate that LCE has excellent predictive capabilities and provides valuable guidance for sepsis prediction. The experimental results indicate that LCE prediction accuracy achieves 97.5%, with AUROC reaching 99.2% and AUPR reaching 98.3%. LCE is then employed to predict sepsis in ICU patients and investigate the most accurate time frame for sepsis prediction. Initially, XGBoost is used for feature selection to identify the optimal feature subset.

Therefore, this paper proposes an XGB-LCE model for early sepsis prediction in ICU patients. However, existing prediction methods have limitations, such as difficulties in processing features and inaccurate prediction. Recently, many researchers have attempted to predict sepsis using machine learning algorithms. Nevertheless, early recognition of sepsis is challenging as it is difficult to define and diagnose clinically. Early detection and intervention are critical to improving the prognosis of patients with sepsis, with each hour of delay in treatment increasing mortality by 4–8%.

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