Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the...
BACKGROUND: Mild cognitive impairment (MCI) represents a transitional stage to Alzheimer's disease (AD), making progression prediction crucial for timely intervention. Predictive models integrating clinical, laboratory, and survival data can enhance ...
BACKGROUND: Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guid...
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.
BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction.
BACKGROUND: Current cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation.
Gastric cancer (GC) is a highly heterogeneous disease that requires highly accurate prognostic models. Machine learning is a powerful tool for identifying predictive biomarkers and developing prognostic models. Here, we aim to integrate bioinformatic...
BACKGROUND: There is still no clinical biomarker to diagnose depression. Given the complexity of a multifactorial disease like depression, a single biomarker is unlikely to capture the full heterogeneity of the disease and be applicable in clinical p...
Atrial fibrillation (AF), the most prevalent critical care arrhythmia, demonstrates substantial mortality associations where renal dysfunction management plays a pivotal therapeutic role. We examined the prognostic capacity of admission blood urea ni...
This study examined the predictive performance of cardiovascular disease (CVD)-specific mortality using traditional statistical and machine learning models with non-invasive indicators, and assessed whether adding blood lipid profiles improves predic...
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