AIMC Topic: Machine Learning

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Alterations in the functional MRI-based temporal brain organisation in individuals with obesity.

Diabetes, obesity & metabolism
AIMS: Obesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity have been widely studied, the temporal dynamics in their brains remain poorly understood. Therefore,...

Generative adversarial network augmented data for improved heart sound abnormality detection.

Computers in biology and medicine
The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 dataset has driven significant advancements in automated heart sound analysis using machine learning (ML) and deep learning (DL). However, these efforts are constrained by the dataset's limi...

Short Research Article: Evaluation of an artificial intelligence language model in psychiatric patient education.

Child and adolescent mental health
BACKGROUND: The incorporation of artificial intelligence (AI) and machine learning (ML) into medicine has enhanced clinical information processing. ChatGPT, an AI language model, has demonstrated proficiency in generating human-like responses to comp...

Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi-omics study integrating machine learning, mendelian randomization and mediation analysis.

Diabetes, obesity & metabolism
BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study aims to identify diagnostic biomarkers and explore potential gene-metabolite interactions in DN patho...

MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.

Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

Ecotoxicology and environmental safety
BACKGROUND: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure.

A multidimensional prediction model for overtraining risk in youth soccer players: Integrating physiological and psychological markers.

Journal of sports sciences
Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with high training demands. This study aimed to develop and validate a multidimensional prediction model fo...

International Consensus Histopathological Criteria for Subtyping Idiopathic Multicentric Castleman Disease Based on Machine Learning Analysis.

American journal of hematology
Idiopathic multicentric Castleman disease (iMCD) is a rare lymphoproliferative disorder classified into three recognized clinical subtypes-idiopathic plasmacytic lymphadenopathy (IPL), TAFRO, and NOS. Although clinical criteria are available for subt...

Advancing biogeographical ancestry predictions through machine learning.

Forensic science international. Genetics
Tools like Snipper or the Admixture Model count as state-of-the-art methods in forensic science for biogeographical ancestry. However, they have not been systematically compared to classifiers widely used in other disciplines. Noting that genetic dat...