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.