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All Outputs (6)

Clinical and Psychological Factors Associated with Frailty in Patients with Heart Failure (2024)
Journal Article
Żółkowska, B., Lee, C. S., Denfeld, Q. E., Jędrzejczyk, M., Diakowska, D., Lisiak, M., Wleklik, M., Czapla, M., & Uchmanowicz, I. (2024). Clinical and Psychological Factors Associated with Frailty in Patients with Heart Failure. Journal of Clinical Medicine, 13(23), Article 7345. https://doi.org/10.3390/jcm13237345

Background/Objectives: Heart failure (HF) is a significant public health issue with high morbidity and mortality rates. This study aims to investigate the interrelationships between frailty, cognitive impairment, and depression in older adults with H... Read More about Clinical and Psychological Factors Associated with Frailty in Patients with Heart Failure.

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis (2024)
Journal Article
Lewandowski, Ł., Czapla, M., Uchmanowicz, I., Kubielas, G., Zieliński, S., Krzystek-Korpacka, M., Ross, C., Juárez-Vela, R., & Zielińska, M. (2024). Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis. Medical Science Monitor, 30, Article e944408. https://doi.org/10.12659/msm.944408

BACKGROUND: Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA.

MATERI... Read More about Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

Application of machine learning in predicting frailty syndrome in patients with heart failure (2024)
Journal Article
Szczepanowski, R., Uchmanowicz, I., Pasieczna-Dixit, A. H., Sobecki, J., Katarzyniak, R., Kołaczek, G., Lorkiewicz, W., Kędras, M., Dixit, A., Biegus, J., Wleklik, M., Gobbens, R. J., Hill, L., Jaarsma, T., Hussain, A., Barbagallo, M., Veronese, N., Morabito, F. C., & Kahsin, A. (2024). Application of machine learning in predicting frailty syndrome in patients with heart failure. Advances in Clinical and Experimental Medicine, 33(3), 309-315. https://doi.org/10.17219/acem/184040

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more sa... Read More about Application of machine learning in predicting frailty syndrome in patients with heart failure.

Rationing of nursing care in Internal Medicine Departments—a cross-sectional study (2023)
Journal Article
Jędrzejczyk, M., Guzak, B., Czapla, M., Ross, C., Vellone, E., Juzwiszyn, J., Chudiak, A., Sadowski, M., & Uchmanowicz, I. (2023). Rationing of nursing care in Internal Medicine Departments—a cross-sectional study. BMC Nursing, 22(1), Article 455. https://doi.org/10.1186/s12912-023-01617-x

Background: Implicit rationing of nursing care refers to a situation in which necessary nursing care is not performed to meet all of the patients’ needs. Purpose: To examine the factors influencing the rationing of nursing care, nurses’ assessment of... Read More about Rationing of nursing care in Internal Medicine Departments—a cross-sectional study.