TY - JOUR
T1 - A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections
AU - de Vries, Sjoerd
AU - ten Doesschate, Thijs
AU - Totté, Joan E.E.
AU - Heutz, Judith W.
AU - Loeffen, Yvette G.T.
AU - Oosterheert, Jan Jelrik
AU - Thierens, Dirk
AU - Boel, Edwin
N1 - Funding Information:
This work was supported by the University Medical Center Utrecht . This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2022 The Authors
PY - 2022/7
Y1 - 2022/7
N2 - Urinary Tract Infections (UTIs) are among the most frequently occurring infections in the hospital. Urinalysis and urine culture are the main tools used for diagnosis. Whereas urinalysis is sufficiently sensitive for detecting UTI, it has a relatively low specificity, leading to unnecessary treatment with antibiotics and the risk of increasing antibiotic resistance. We performed an evaluation of the current diagnostic process with an expert-based label for UTI as outcome, retrospectively established using data from the Electronic Health Records. We found that the combination of urinalysis results with the Gram stain and other readily available parameters can be used effectively for predicting UTI. Based on the obtained information, we engineered a clinical decision support system (CDSS) using the reliable semi-supervised ensemble learning (RESSEL) method, and found it to be more accurate than urinalysis or the urine culture for prediction of UTI. The CDSS provides clinicians with this prediction within hours of ordering a culture and thereby enables them to hold off on prematurely prescribing antibiotics for UTI while awaiting the culture results.
AB - Urinary Tract Infections (UTIs) are among the most frequently occurring infections in the hospital. Urinalysis and urine culture are the main tools used for diagnosis. Whereas urinalysis is sufficiently sensitive for detecting UTI, it has a relatively low specificity, leading to unnecessary treatment with antibiotics and the risk of increasing antibiotic resistance. We performed an evaluation of the current diagnostic process with an expert-based label for UTI as outcome, retrospectively established using data from the Electronic Health Records. We found that the combination of urinalysis results with the Gram stain and other readily available parameters can be used effectively for predicting UTI. Based on the obtained information, we engineered a clinical decision support system (CDSS) using the reliable semi-supervised ensemble learning (RESSEL) method, and found it to be more accurate than urinalysis or the urine culture for prediction of UTI. The CDSS provides clinicians with this prediction within hours of ordering a culture and thereby enables them to hold off on prematurely prescribing antibiotics for UTI while awaiting the culture results.
KW - Antibiotic stewardship
KW - Clinical decision support
KW - Ensemble learning
KW - RESSEL
KW - Semi-supervised learning
KW - Urinary tract infection
UR - http://www.scopus.com/inward/record.url?scp=85130629023&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2022.105621
DO - 10.1016/j.compbiomed.2022.105621
M3 - Article
C2 - 35617725
AN - SCOPUS:85130629023
SN - 0010-4825
VL - 146
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 105621
ER -