DOI: 10.5061/dryad.6djh9w107
title: Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study

description: 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.
description: Retrospective cohort analysis of consecutive patient admissions
 in a Danish region over a 16 months period beginning January
 2017. Data were obtained from the four public
 somatic hospital trusts in the Region of Southern Denmark and comprised
 301,932 adult admissions. Data are extracted from a
 regional data warehouse containing 1) a copy of EMR notes from the four
 hospital trusts covering a population of 1.2 million inhabitants; 2)
 administrative data on all hospital admissions; 3) positive culture
 results from the four clinical microbiology laboratories in the region.
 The unique patient civil registration number used for all health contacts
 in Denmark enables linkage between the various public healthcare
 registries and the construction of the medical history of a
 patient. Data types (predictor variables) include
 dates and place of admission, demographics, historical diagnosis codes,
 data from an automatic electronic infection registry, trigger based text
 mining from the electronic medical records in hospital (EMR). Risk factor
 variables chosen were based upon review of the literature. The target
 variable is any urinary tract infection (UTI) detected during admission.
 Urinary tract infection is predominantly diagnosed based on a combination
 of clinical features, culture of significant amounts of bacterial
 pathogens in urine, and relevant antimicrobial treatment. Information
 about UTIs is copied from the automated electronic hospital infection
 monitoring system HAIR used in the Region of Southern
 Denmark. The primary goal of our modelling
 approach is to find patterns/relations in data between the dependent
 variable (UTI) and the independent variables (predictor variables or risk
 factors). For UTI prediction, we use five different machine-learning
 algorithms: Neural Networks, Gradient Boosting, Regression, Decision Tree
 3 Way Split, and Decision Tree. All models are on target Any UTI (HA-UTI
 or CA-UTI), yes (=1) versus no (=0). The development data set has been
 partitioned into 60 % for training and 40 % for validation. Assessment and
 comparisons of models are based on average squared error, ROC-index, and
 cumulative Lift. SAS
 Enterprise Miner 14.3 has been used for the model development. Variable
 selection has been done using automate variable selection procedures, such
 as Stepwise Selection for Regression Models and Log-Worth for Decision
 Trees. For Neural Networks and Gradient Boosting, auto-tuning options have
 been used.
description: Missing values for admission_hospital_text (hospital id) for 240
 of 301932 admission cases.