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Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.
Journal of cancer research and clinical oncology
Sep 16, 2024
BACKGROUND: This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques.
Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.
Physiological measurement
Sep 16, 2024
We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. T...
Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis.
Food & function
Sep 16, 2024
Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molecular understanding of MI is elusive and needs to be fully explored. This study aimed to elucidate th...
Predicting hypoglycemia in ICU patients: a machine learning approach.
Expert review of endocrinology & metabolism
Sep 16, 2024
BACKGROUND: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan.
Machine learning approaches to identify the link between heavy metal exposure and ischemic stroke using the US NHANES data from 2003 to 2018.
Frontiers in public health
Sep 16, 2024
PURPOSE: There is limited understanding of the link between exposure to heavy metals and ischemic stroke (IS). This research aimed to develop efficient and interpretable machine learning (ML) models to associate the relationship between exposure to h...
Development of a machine learning model to estimate length of stay in coronary artery bypass grafting.
Revista de saude publica
Sep 16, 2024
OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.
Investigating artificial intelligence models for predicting joint pain from serum biochemistry.
Revista da Associacao Medica Brasileira (1992)
Sep 16, 2024
OBJECTIVE: The study used machine learning models to predict the clinical outcome with various attributes or when the models chose features based on their algorithms.
A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 16, 2024
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, al...
Clinical feasibility of deep learning-accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla.
European journal of radiology
Sep 15, 2024
PURPOSE: To assess the feasibility of the single-shot turbo spin echo sequence using deep learning-based reconstruction (DLR) (HASTE) with enhanced denoising for pancreas MRI.
Clinical evaluation of a machine learning-based early warning system for patient deterioration.
CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Sep 15, 2024
BACKGROUND: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-ti...
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