OBJECTIVE: To develop an automated method for the joint and consistent evaluation of emphysema and mortality risk that provides quantification of data and model uncertainty.
OBJECTIVES: Machine learning (ML) is an emerging discipline centered around complex pattern matching and large data-based prediction modeling and can improve precision medicine healthcare. Cochlear implants (CI) are highly effective, however, outcome...
PURPOSE: To validate a deep learning (DL) framework for detecting and quantifying cystoid fluid collections (CFC) on spectral-domain optical coherence tomography (SD-OCT) in X-linked retinoschisis (XLRS) patients.
In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building on this insight, we develop...
International journal of medical informatics
Apr 4, 2025
BACKGROUND: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable prediction model undergoing a rigorous and standardized process is essential for clinical decision-...
BACKGROUND: This research aimed to explore the efficacy and safety of electroacupuncture in promoting the recovery of postoperative gastrointestinal function and to discuss the potential mechanism on the basis of heart rate variability (HRV).
BACKGROUND: Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underlying biological mechanisms responsible for cortical changes in the AD cortex remain unclear.
Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-li...
BACKGROUND: Finding a biomarker to diagnose migraine remains a significant challenge in the headache field. Migraine patients exhibit dynamic and recurrent alterations in the brainstem-thalamo-cortical loop, including reduced thalamocortical activity...
This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fatty, scattered, heterogeneously dense, and extremely dense). A retrospective analysis was conducted ...
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