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The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.
Cardiovascular diabetology
Jan 29, 2025
BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains cha...
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC cancer
Jan 29, 2025
BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
A deep learning analysis for dual healthcare system users and risk of opioid use disorder.
Scientific reports
Jan 29, 2025
The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an inc...
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
Scientific reports
Jan 29, 2025
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasi...
Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.
JMIR formative research
Jan 29, 2025
BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from ...
Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR medical informatics
Jan 29, 2025
BACKGROUND: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variati...
Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.
Frontiers in immunology
Jan 29, 2025
PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based mac...
Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.
Annals of surgical oncology
Jan 28, 2025
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.
Health services research
Jan 28, 2025
OBJECTIVE: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.
Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays.
Journal of occupational and environmental medicine
Jan 28, 2025
OBJECTIVE: The aims of the study were to develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs and to assess its predictive capacity.
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