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Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis.
J Inflamm Res
January 2025
Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan Province, People's Republic of China.
Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.
View Article and Find Full Text PDFGenerative artificial intelligence in graduate medical education.
Front Med (Lausanne)
January 2025
Clinical Informatics Fellowship Program, Baylor Scott & White Health, Round Rock, TX, United States.
Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potential opportunities and risks of GenAI in graduate medical education (GME). We review the existing literature and provide commentary on how GenAI could impact GME, including five key areas of opportunity: electronic health record (EHR) workload reduction, clinical simulation, individualized education, research and analytics support, and clinical decision support.
View Article and Find Full Text PDFPredictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit.
Front Med (Lausanne)
January 2025
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Background: Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.
Methods: All data utilized in this study were sourced from the MIMIC-IV database.
Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study.
Front Immunol
January 2025
Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.
Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital.
Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer.
Front Immunol
January 2025
School of Nursing, Zunyi Medical University, Zunyi, China.
Background: Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.
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