AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus

Abstract

The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasaki disease, toxic shock syndrome, and endemic typhus. Endemic typhus, or murine typhus, is an acute infection treated much differently than MIS-C, so early detection is crucial to a favorable prognosis for patients with these disorders. Clinical Decision Support Systems (CDSS) are computer systems designed to support the decision-making of medical teams about their patients and intended to improve uprising clinical challenges in healthcare. In this article, we present a CDSS to distinguish between MIS-C and typhus, which includes a scoring system that allows the timely distinction of both pathologies using only clinical and laboratory features typically available within the first six hours of presentation to the Emergency Department. The proposed approach was trained and tested on datasets of 87 typhus patients and 133 MIS-C patients. A comparison was made against five well-known statistical and machine-learning models. A second dataset with 111 MIS-C patients was used to verify the effectiveness and robustness of the AI-MET system. The performance assessment for AI-MET and the five statistical and machine learning models was performed by computing sensitivity, specificity, accuracy, and precision. The AI-MET system scores 100 percent in the five metrics used on the training and testing dataset and 99 percent on the validation dataset. Statistical analysis tests were also performed to evaluate the robustness and ensure a thorough and balanced evaluation, in addition to demonstrating the statistical significance of MET-30 performance compared to the baseline models.