Sensitivity analysis–based sepsis prognosis using artificial intelligence

Abstract

Sepsis is a life-threatening condition which may arise from an extreme response of the body to an infection. While worldwide figures are unknown, it is estimated that sepsis causes millions of yearly fatalities. In this context, it is important to develop tools for decision support and training of healthcare professionals. This paper proposes that artificial intelligence tools be used for prognosis of septic patients. The model used is a neural network trained and validated with cross-validation. The information used includes data regarding patient history and treatment. More importantly, this paper also presents a principled approach of using sensitivity analysis for the identification of discriminatory variables when these are of mixed types such as binary, categorical, and integer. While initial results, validated on over 5000 patients, already show both specificity and sensitivity above 80% and good model robustness against errors in most inputs, even better performance is attained through the utilization of sensitivity analysis to select the variables used as inputs. This work presents a promising tool for input selection in contexts of limited data availability, and successfully applies this technique to obtain a high-performance model for prognosis of septic patients.