BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retros...
BACKGROUND: Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately disc...
BACKGROUND: Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced s...
Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage non-small-cell lung cancer (NSCLC), significantly affecting patient outcomes. Conventional computed tomography (CT) often fails to diagnose VPI accurately. This retrospect...
BACKGROUND: Sepsis is a severe and frequent complication among ischemic stroke patients during hospitalization. The atherogenic index of plasma (AIP), as metabolism-related markers, are closely linked to inflammation. However, their relationship with...
BACKGROUND: Accurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanc...
This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 patients with T2DM w...
OBJECTIVES: This study aimed to develop robust machine learning (ML)-based and deep learning (DL)-based models capable of detecting mpox cases for surveillance efforts using clinical notes.
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