AIMC Topic: Machine Learning
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Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production.
NPJ systems biology and applications
Jul 31, 2025
Universal differential equations (UDEs) are an emerging approach in biomedical systems biology, integrating physiology-driven mathematical models with machine learning for data-driven model discovery in areas where knowledge of the underlying physiol...
Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation.
Nature communications
Jul 31, 2025
Atrial fibrillation (AF) increases the risk of adverse cardiovascular events, yet the underlying biological mechanisms remain unclear. We evaluate a panel of 12 circulating biomarkers representing diverse pathophysiological pathways in 3817 AF patien...
Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health.
Journal of medical Internet research
Jul 31, 2025
The role and use of race within health-related artificial intelligence (AI) and machine learning (ML) models have sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to g...
Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Jul 31, 2025
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) share similar clinical, laboratory, and imaging features, making accurate diagnosis challenging. Nevertheless, PDAC is highly malignant with a poor prognosis, whe...
Automating Colon Polyp Classification in Digital Pathology by Evaluation of a "Machine Learning as a Service" AI Model: Algorithm Development and Validation Study.
JMIR formative research
Jul 31, 2025
BACKGROUND: Artificial intelligence (AI) models are increasingly being developed to improve the efficiency of pathological diagnoses. Rapid technological advancements are leading to more widespread availability of AI models that can be used by domain...
Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.
Scientific reports
Jul 31, 2025
Myocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admitted to the intensive care unit (ICU) with myocardial infarction are at higher risk of death. In this study, we use two retrospective cohorts extracted...
A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts.
Nanoscale
Jul 31, 2025
Nucleoside drugs, mimics of natural nucleosides, have become cornerstone treatments in clinical approaches to combat cancer and viral infections. The analysis of nucleoside drugs is commonly performed using liquid chromatography-tandem mass spectrome...
Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis.
Human vaccines & immunotherapeutics
Jul 30, 2025
Exploring the influencing factors of COVID-19 vaccine hesitancy and summarizing countermeasures is of great significance for effectively addressing potential public health crises. Based on survey data from China, we employed a Gradient Boosting Decis...
Ultrasound-based machine learning models for predicting response to neoadjuvant chemotherapy in breast cancer: A meta-analysis.
Clinical imaging
Jul 30, 2025
BACKGROUND AND AIMS: Breast cancer remains the most common cancer among women globally, with neoadjuvant chemotherapy (NAC) serving as a critical pre-surgical intervention. Ultrasound-based radiomics and machine learning (ML) models offer potential f...
Efficacy of image similarity as a metric for augmenting small dataset retinal image segmentation.
Computers in biology and medicine
Jul 30, 2025
Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthetic image quality is the Fréchet Inception Distance (FID) which measure...
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