AIMC Topic: Machine Learning
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Performance of Clinical Risk Prediction Models for Post-ERCP Pancreatitis: A Systematic Review.
Pancreas
Aug 1, 2025
OBJECTIVES: Pancreatitis is common following endoscopic retrograde cholangiopancreatography (ERCP). Despite increased vigilance of post-ERCP pancreatitis (PEP), both its incidence and associated mortality are rising. Risk prediction models may provid...
An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation.
Annals of medicine
Jul 31, 2025
OBJECTIVES: During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavourable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implant...
Applying stacked machine learning models to guide electrochemical oxidation of antibiotics: Key parameter identification and process optimization insights.
Journal of environmental management
Jul 31, 2025
The continuous accumulation of antibiotics in the environment has become an increasingly concerned global environmental problem. Electrochemical advanced oxidation processes (EAOPs) have been attracted much attention in antibiotic degradation due to ...
Anemia prediction using gene expression programming (GEP) and explainable artificial intelligence approaches.
Computers in biology and medicine
Jul 31, 2025
Anemia being a global health disorder, affecting millions of people, especially pregnant women, children, and the elderly. Proper and timely diagnosis must be ensured to prevent its adverse effects, but the traditional diagnostic methods are very tim...
Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification.
BMC medical informatics and decision making
Jul 31, 2025
Thyroid disease classification is a critical challenge in medical diagnostics, requiring accurate differentiation between hyperthyroidism, hypothyroidism, and normal thyroid function. This study introduces an advanced machine learning approach that i...
Intraoperative hypotension prediction in cardiac and noncardiac procedures: is HPI truly worthwhile? A systematic review and meta-analysis.
BMC anesthesiology
Jul 31, 2025
BACKGROUND: Intraoperative hypotension (IOH), defined as a mean arterial pressure (MAP) below 65 mmHg, is a common complication during surgery and is associated with significant postoperative morbidity, including acute kidney injury, myocardial injur...
Using a large language model (ChatGPT) to assess risk of bias in randomized controlled trials of medical interventions: protocol for a pilot study of interrater agreement with human reviewers.
BMC medical research methodology
Jul 31, 2025
BACKGROUND: Risk of bias (RoB) assessment is an essential part of systematic reviews that requires reading and understanding each eligible trial and RoB tools. RoB assessment is subject to human error and is time-consuming. Machine learning-based too...
Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.
BMC medicine
Jul 31, 2025
BACKGROUND: Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practic...
A retrospective cohort study using machine learning to predict coronary artery lesions in children with Kawasaki disease.
BMC pediatrics
Jul 31, 2025
BACKGROUND: Kawasaki disease (KD) mainly occurs in children under 5 years old, and the most common complication of KD is coronary artery lesion (CAL). In recent years, the incidence rate of KD has increased year by year worldwide, so it is particular...
A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.
BMC medical imaging
Jul 31, 2025
This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas...
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