OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.
A cerebral aneurysm may present irregularities associated with rupture risks. However, conventional morphological parameters are limited in evaluating the aneurysm irregularity. Although the mass moment of inertia has been devised for the irregularit...
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...
BACKGROUND: Reactivation of latent tuberculosis infection (LTBI) is a major obstacle to tuberculosis eradication. Predicting LTBI relapse is crucial for effective disease management but remains underexplored.
The medical community demands accurate predictive models for early heart disease diagnosis because heart disease remains a significant worldwide health concern. Deep learning research presents a predictive system for heart disease that uses K-mode cl...
Abdominal aortic aneurysm (AAA) progression carries a significant rupture risk, demanding accurate prediction models beyond traditional methods that rely on limited clinical parameters and often overlook complex factor interplay. We aimed to enhance ...
Standard episodic patient monitoring of vital signs on the medical-surgical wards can potentially miss changes in health status and delay recognition of risk. To reduce these delays, we develop a clinical wearable-based deep learning model, using 9 i...
This study aimed to apply a neural network to raw bioelectrical impedance analysis data and to test whether sarcopenia could be predicted with high accuracy. The study population comprised 727 community-dwelling older adults aged 65-85 years who part...
BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients.
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...
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