BACKGROUND: The objective of this study was to evaluate the concordance between therapeutic recommendations proposed by a multidisciplinary team meeting and those generated by a large language model (ChatGPT) for colorectal cancer. Although multidisc...
Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-I...
Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important biomarkers for risk assessment. However, long acquisition times and cumbersome data analysis limit wide...
OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality predic...
International journal of molecular sciences
40332516
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...
IMPORTANCE: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on th...
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...
BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both envir...
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk as...