AIMC Topic: Aged
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Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.
Annals of hepatology
Aug 15, 2024
INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning...
Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.
PloS one
Aug 15, 2024
Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associated with a longer stay in the post-anesthesia care unit, readmissions, and perioperative costs. This ...
Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.
Internal and emergency medicine
Aug 14, 2024
Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive...
Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.
JACC. Cardiovascular imaging
Aug 14, 2024
BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to severe AS or by the effects of comorbidities. In the absence of randomized trial evidence favoring aortic valve replacement (AVR) in patients with moderat...
Middle Aged
Predictive Value of Tests
Humans
Heart Failure
Risk Assessment
Phenotype
Aged
Comorbidity
Prognosis
Cluster Analysis
Aged, 80 and over
Severity of Illness Index
Female
Male
Aortic Valve Stenosis
Time Factors
Reproducibility of Results
Echocardiography
Aortic Valve
Risk Factors
Unsupervised Machine Learning
Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT.
Ophthalmology
Aug 14, 2024
PURPOSE: To quantify morphological changes of the photoreceptors (PRs) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of OCT images.
Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.
European radiology
Aug 14, 2024
OBJECTIVES: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.
An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading.
JACC. Cardiovascular imaging
Aug 14, 2024
BACKGROUND: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnosti...
Deep learning segmentation of mandible with lower dentition from cone beam CT.
Oral radiology
Aug 14, 2024
OBJECTIVES: This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.
Using machine learning to explore the predictors of life satisfaction trajectories in older adults.
Applied psychology. Health and well-being
Aug 14, 2024
Life satisfaction is vital for older adults' well-being, impacting various life aspects. It is dynamic, necessitating nuanced approaches to capture its trajectories accurately. This study aimed to explore the diverse trajectories and predictors of li...
Computer-Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Aug 14, 2024
OBJECTIVES: One of the most promising adjuncts for screening breast cancer is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a ...
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