Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of Resistant Infections.

Drug-resistant Gram-negative bacterial infections, on average, increase the length of stay (LOS) in U.S. hospitals by 5 days, translating to approximately $15,000 per patient.

Individualized, discrete event, simulations provide insight into inter- and intra-subject variability of extended-release, drug products.

Objective: Develop and validate particular, concrete, and abstract yet plausible in silico mechanistic explanations for large intra- and interindividual variability observed for eleven bioequivalence study participants. Do so in the face of considerable uncertainty about mechanisms.

Methods: We constructed an object-oriented, discrete event model called subject (we use small caps to distinguish computational objects from their biological counterparts).