Supervised Classification Techniques for Prediction of Mortality in Adult Patients with Sepsis

Background: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may increase the accuracy for clinical decisions.

Objective: To evaluate several supervised techniques of Artificial Intelligence (AI) for classification and prediction of mortality, in adult patients hospitalized by emergency services with sepsis diagnosis.

Methods: Secondary data analysis of a prospective cohort in three university hospitals in Medellín, Colombia. We included patients >18 years hospitalized for suspected or confirmed infection and any organ dysfunction according to the Sepsis-related Organ Failure Assessment. The outcome variable was hospital mortality and the prediction variables were grouped into those related to the initial clinical treatment and care or to the direct measurement of physiological disturbances. Four supervised classification techniques were analyzed: the C4.5 Decision Tree, Random Forest, artificial neural networks (ANN) and support vector machine (SVM) models. Their performance was evaluated by the concordance between the observed and predicted outcomes and by the discrimination according to AUC-ROC.

Results: A total of 2510 patients with a median age of 62 years (IQR = 46-74) and an overall hospital mortality rate of 11.5% (n = 289). The best discrimination was provided by the SVM and ANN using physiological variables, with an AUC-ROC of 0.69 (95%CI: 0.62; 0.76) and AUC-ROC of 0.69 (95%CI: 0.61; 0.76) respectively.

Conclusion: Deep learning and AI are increasingly used as support tools in clinical medicine. Their performance in a syndrome as complex and heterogeneous as sepsis may be a new horizon in clinical research. SVM and ANN seem promising for improving sepsis classification and prognosis.