The Artificial Neural Network as a Diagnostic Tool of the Risk of Clostridioides difficile Infection among Patients with Chronic Kidney Disease.
Jakub StojanowskiAndrzej KoniecznyŁukasz LisWeronika FrosztęgaPatrycja BrzozowskaAnna CiszewskaKlaudia RydzyńskaMichał SrokaKornelia KrakowskaTomasz GołębiowskiZbigniew HrubyMariusz Andrzej KusztalMagdalena KrajewskaPublished in: Journal of clinical medicine (2023)
The majority of recently published studies indicate a greater incidence and mortality due to Clostridioides difficile infection (CDI) in patients with chronic kidney disease (CKD). Hospitalization, older age, the use of antibiotics, immunosuppression, proton pump inhibitors (PPI), and chronic diseases such as CKD are responsible for the increased prevalence of infections. The aim of the study is to identify clinical indicators allowing, in combination with artificial intelligence (AI) techniques, the most accurate assessment of the patients being at elevated risk of CDI.
Keyphrases
- artificial intelligence
- clostridium difficile
- chronic kidney disease
- end stage renal disease
- neural network
- risk factors
- machine learning
- big data
- deep learning
- newly diagnosed
- ejection fraction
- physical activity
- peritoneal dialysis
- prognostic factors
- randomized controlled trial
- type diabetes
- cardiovascular events
- mass spectrometry
- middle aged
- coronary artery disease