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Re-hospitalization factors and economic characteristics of urinary tract infected patients using machine learning.
Lee, Yul Hee; Baik, Young Seo; Kim, Young Jae; Shi, Hye Jin; Moon, Jong Youn; Kim, Kwang Gi.
Afiliación
  • Lee YH; Department of Nursing, Gachon University, Incheon, Korea.
  • Baik YS; Department of Biomedical Engineering, Gachon University, Republic of Korea.
  • Kim YJ; Gachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon, Korea.
  • Shi HJ; Division of Infectious Disease, Department of Internal Medicine, Gachon University College of Medicine, Gil Medical Center, Incheon, Korea.
  • Moon JY; Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea.
  • Kim KG; Center for Public Healthcare, Gachon University Gil Medical Center, Incheon, Korea.
Digit Health ; 10: 20552076241272697, 2024.
Article en En | MEDLINE | ID: mdl-39130518
ABSTRACT

Objective:

Urinary tract infection is one of the most prevalent bacterial infectious diseases in outpatient treatment, and 50-80% of women experience it more than once, with a recurrence rate of 40-50% within a year; consequently, preventing re-hospitalization of patients is critical. However, in the field of urology, no research on the analysis of the re-hospitalization status for urinary tract infections using machine learning algorithms has been reported to date. Therefore, this study uses various machine learning algorithms to analyze the clinical and nonclinical factors related to patients who were re-hospitalized within 30 days of urinary tract infection.

Methods:

Data were collected from 497 patients re-hospitalized for urinary tract infections within 30 days and 496 patients who did not require re-hospitalization. The re-hospitalization factors were analyzed using four machine learning algorithms gradient boosting classifier, random forest, naive Bayes, and logistic regression.

Results:

The best-performing gradient boosting classifier identified respiratory rate, days of hospitalization, albumin, diastolic blood pressure, blood urea nitrogen, body mass index, systolic blood pressure, body temperature, total bilirubin, and pulse as the top-10 factors that affect re-hospitalization because of urinary tract infections. The 993 patients whose data were collected were divided into risk groups based on these factors, and the re-hospitalization rate, days of hospitalization, and medical expenses were observed to decrease from the high- to low-risk group.

Conclusions:

This study showed new possibilities in analyzing the status of urinary tract infection-related re-hospitalization using machine learning. Identifying factors affecting re-hospitalization and incorporating preventable and reinforcement-based treatment programs can aid in reducing the re-hospitalization rate and average number of days of hospitalization, thereby reducing medical expenses.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Digit Health Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Digit Health Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos