Background

Urinary tract infections (UTIs) represent one of the most common complaints faced by healthcare providers in inpatient and outpatient settings. It is a common indication for antibiotic treatment, but overuse of broad spectrum therapies has selected for antimicrobial resistant pathogens. With this in mind, clinicians send urine specimens to the microbiology laboratory to conduct antibiotic susceptibility testing.

The receipt of definitive data from the microbiology laboratory, however, can take as long as 72 hours to return, and an antibiotic must be chosen in the meantime. This situation is referred to as empiric antibiotic treatment. When selecting an antibiotic therapy, providers must balance between the goal of using narrow spectrum antibiotics, while avoiding inappropriate antibiotic therapy (the selection of an antibiotic to which the patient is resistant).

This dataset is designed to support the development of algorithms to guide empiric treatment decisions in the context of uncomplicated UTIs, helping providers to choose effective antibiotics while avoiding the overuse of broad spectrum therapies.

Because antibiotic susceptibility testing provides a proxy for counterfactual outcomes under different treatments, this dataset supports the development and validation of causal inference and policy learning methods more broadly. To support the study of transfer learning, we also include a broader cohort of more complicated UTIs.