Study Data from Texas State University Update Knowledge of Machine Learning (Predicting High Urinary Tract Infection Rates in Skilled Nursing Facilities: A Machine Learning Approach): Machine Learning

2025 NOV 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Data detailed on Machine Learning have been presented. According to news reporting from Round Rock, Texas, by NewsRx journalists, research stated, “: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics.”

Financial support for this research came from Texas State University Williamson.

The news correspondents obtained a quote from the research from Texas State University, “We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models.”

According to the news reporters, the research concluded: “Results from this study can inform infection prevention efforts in post-acute care settings.”

For more information on this research see: Predicting High Urinary Tract Infection Rates in Skilled Nursing Facilities: A Machine Learning Approach. Healthcare, 2025;13(20):2632. Healthcare can be contacted at: Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland. (CSIRO Publishing - www.publish.csiro.au; Healthcare - http://www.publish.csiro.au/nid/241.htm)

Our news journalists report that additional information may be obtained by contacting Tiankai Wang, Health Informatics & Information Management Department, Texas State University, Round Rock, TX 78665, United States. Additional authors for this research include Diane Dolezel and Denise Gobert.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.3390/healthcare13202632. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal Healthcare is: Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland.

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