Sepsis Treatment Recommendation Using Sensitivity to Input Medicine Dosage in Deep Neural Networks

Abstract

Sepsis is a life-threatening condition that ranks among the foremost global causes of mortality. Its treatment is marked by significant expenses and the incorporation of diverse symptomatology. Consequently, an array of investigative efforts has been dedicated to sepsis, spanning the classification of its stages, early detection, prognosis prediction, and therapeutic recommendations. Notably, the complex and contentious nature of sepsis management underscores the necessity for precision in combination therapies. In this research endeavor, this study proposes an advanced methodology for sepsis treatment recommendations grounded in deep neural networks. The approach entails the construction of an ensemble deep learning model geared towards predicting the subsequent Sequential Organ Failure Assessment (SOFA) score. Employing this trained ensemble model, the study embarks on the task of optimizing sepsis treatment dosages. The empirical results conclusively demonstrate the superior performance of the proposed ensemble model relative to those of the conventional methods, signifying its capacity to offer treatment prescriptions akin to or surpassing those rendered by medical practitioners. The model consistently outperforms the alternative approaches in predicting the SOFA score and aligns the treatment recommendations with those of medical professionals, exhibiting a high degree of similarity. This innovative approach holds promise for advancing personalized medicine and improving patients’ outcomes in sepsis treatment.