Detection of sepsis using biomarkers based on machine learning

Abstract

BACKGROUND: Sepsis is the second most common cause of death in patients with non-cardiovascular diseases admitted to the ICU. It is one of the top ten reasons for death among all hospitalized patients. This study aimed to compare the value of some blood parameters in diagnosing sepsis and investigate their relationship to select a more practical diagnostic method. METHODS: In this descriptive-analytical study, 208 patients with sepsis admitted to the ICU were selected. Then the physiological parameters of patients and normal individuals were measured. Data analysis was performed using the p value and effect size methods and MATLAB software. To classify the disease, the MLP, RBF, and KNN methods were used. RESULTS: The values of the HR, O2Sat, and SBP in patients with sepsis have changed signifi cantly compared to NORMAL conditions. The classifi cation results using different classifi cations showed that the values of specifi city, sensitivity, and accuracy values in the classifi er are more than MLP and RBF and equal to 98 %, 100 %, and 99 %, respectively. CONCLUSIONS: Clinically, accurate detection of sepsis and predicting the patients at risk of developing sepsis is useful for improving treatment. Given the signifi cant differences between HR, O2Sat, and SBP between normal and sepsis patients in this study, it may be possible to use these tests as simple tests instead of the complement protein 3 (C3) and Procalcitonin (PCT) tests to diagnose sepsis in the ICU (Tab. 8, Fig. 10, Ref. 39)