AI-MET: A Deep Learning-based Clinical Decision Support System for Distinguishing Multisystem Inflammatory Syndrome in Children from Endemic Typhus

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 that includes a scoring system that allows the timely distinction of both pathologies only using clinical and laboratory features typically available within the first six hours of presentation to the Emergency Department (ED). 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 AI-MET effectiveness and robustness. The performance assessment for AI-MET and the five statistical and machine learning models was done 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. ### Competing Interest Statement Tiphanie P. Vogel has consulted for Pfizer, Moderna, SOBI, and Novartis and receives research support from AstraZeneca. ### Funding Statement This work was supported in part by R33HD105593. Abraham Bautista is supported by the National Council of Science and Technology of Mexico, scholarship 739528. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The ethics committee/IRB of the University of Houston gave ethical approval for this work with IRB ID of STUDY00003043. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.