“…Mao et al [45] validated an ML algorithm with gradient tree boosting, InSight, providing high sensitivity and specificity for the detection and prediction of sepsis, severe sepsis, and septic shock using the analysis of only six common vital signs taken from EHRs (i.e., systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, peripheral capillary oxygen saturation and temperature). Similar results were confirmed by other authors [46,47]. Other areas in which ML models were successfully applied to diagnostic decision making include influenza [48,49], urinary tract infections [50], chronic obstructive pulmonary disease and asthma exacerbations [51], myocardial infarction [52], appendicitis [53,54].…”