Abstract
BACKGROUND: Digital medicine is an important tool in the current healthcare landscape. Fever is an important reason for evaluating patients at first and second levels of care and a frequent symptom of diseases subject to epidemiological surveillance. OBJECTIVE: To evaluate the diagnostic effectiveness of various algorithms in detecting communicable diseases of epidemiological interest in febrile patients at Hospital General Regional No. 1, Cd. Obregón, Sonora. METHODS: An observational, descriptive, and retrospective study was conducted in a second-level hospital from 1 January 2022 to 31 December 2023, to determine Cohen's kappa and the sensitivity, specificity, positive and negative predictive values, precision and Youden's J index of diagnostic algorithms for 20 communicable diseases with respect to the doctors' diagnoses. RESULTS: Diagnostic algorithms were applied to the data of 909 cases. The sensitivities of Mediktor®, an artificial neural network-based algorithm, a medical diagnostic algorithm and a composite diagnostic algorithm were 11.97%, 64.09%, 69.92% and 99.37%, respectively, and the corresponding specificities were 93.43%, 91.24%, 27.01% and 5.11%, respectively. The neural network-based method yielded the highest Youden's J index. CONCLUSIONS: The medical diagnostic algorithm had the best sensitivity, whereas the specificity was greater for the two artificial intelligence algorithms.