Abstract: A method for identifying two or more infections as related or non-related infections based on an estimated genetic relatedness of the two or more infections, comprising: (i) receiving, for each of two or more infected patients, infection-relevant information comprising an antibiotic resistance profile for the patient's infection, a geo-temporal record for the patient, and a caregiver history for the patient; (ii) estimating, using a trained genetic relatedness model, a genetic relatedness of at least two of the two or more infections; (iii) comparing the estimated genetic relatedness between at least two of the two or more infections to a predetermined threshold; (iv) identifying, based on the comparison, the at least two of the two or more infections as a related infection or a non-related infection.

Abstract: A method for identifying two or more infections as related or non-related infections based on an estimated genetic relatedness of the two or more infections, comprising: (i) receiving, for each of two or more infected patients, infection-relevant information comprising an antibiotic resistance profile for the patient's infection, a geo-temporal record for the patient, and a caregiver history for the patient; (ii) estimating, using a trained genetic relatedness model, a genetic relatedness of at least two of the two or more infections; (iii) comparing the estimated genetic relatedness between at least two of the two or more infections to a predetermined threshold; (iv) identifying, based on the comparison, the at least two of the two or more infections as a related infection or a non-related infection.

Abstract: Various embodiments described herein relate to a method, computer readable medium, and device including one or more of the following: receiving at least one newly-available patient feature for the patient; adding the newly-available patient feature to an indication of all available patient features previously established for a previous application of one of a collection of trained models to the patient; comparing the indication of all available patient features to metadata describing input features of respective models of the collection of trained models to determine whether the input features are available for applying the respective trained models to the patient; selecting a selected trained model based on determining that the input features for the selected trained model are available for applying the selected trained model to the patient; and invoking the selected trained model.