Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study

Objective: The aim of the current study was to develop two predictive models, using data from the index admission as well as historic data on a patient, to predict the development of UTI at the time of entry to the hospital. 

Methods: Retrospective cohort analysis of approx. 300,000 adult admissions in a Danish region was performed. We developed models for UTI prediction with five machine-learning algorithms using demographic information, laboratory results, data on antibiotic treatment, past medical history (ICD10 codes) , and clinical data by transformation of unstructured narrative text in Electronic Medical Records to structured data by Natural Language Processing.

Results: The five machine-learning algorithms have been evaluated by the performance measures average squared error, cumulative lift, and area under the curve (ROC-index). The algorithms had an area under the curve (ROC-index) ranging from 0.82 to 0.84 for the entry model (T = 0 hours after admission).

Conclusion: The study is proof of concept that it is possible to create a machine-learning model that can serve as an early warning system to predict patients at risk of acquiring urinary tract infections during admission. The entry model performs with a high ROC-index indicating a sufficient sensitivity and specificity, which may make the model instrumental in individualized prevention of UTI in hospitalized patients. The favored machine-learning methodology is Decision Trees to ensure the most transparent results and to increase clinical understanding and implementation of the model.