AIMC Topic: Machine Learning

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Comprehensive analysis of coagulation-associated gene signature in bladder cancer diagnosis, prognosis, and immunotherapy.

Scientific reports
In recent years, research on the relationship between coagulation system abnormalities and tumor immunity has been widely reported. Bladder cancer (BC), as an immunogenic tumor, holds great promise in immunotherapy. The role of coagulation-related ge...

Identifying EEG-based neurobehavioral risk markers of gaming addiction using machine learning and iowa gambling task.

Biomedical physics & engineering express
Internet Gaming Disorder (IGD), Gaming Disorder (GD), and Internet Addiction represent behavioral patterns with significant psychological and neurological consequences. Affected individuals often disengage from routine activities and exhibit distress...

Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment.

PloS one
Cardiovascular disease (CVD) is considered the number one killer disease in the world, underlining the importance of the application of more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identifica...

Predictors of childhood vaccination uptake in England: an explainable machine learning analysis of regional data (2021-2024).

Vaccine
BACKGROUND: Childhood vaccination is a cornerstone of public health, yet disparities in vaccination coverage persist across England. These disparities arise from complex interactions among geographic, demographic, socioeconomic, and cultural (GDSC) f...

Artificial intelligence in gut microbiome research: Toward predictive diagnostics for neurodegenerative disorders.

Acta microbiologica et immunologica Hungarica
The human gut microbiota plays a pivotal role in maintaining host immunity, regulating metabolism, and sustaining neurophysiological homeostasis. Increasing evidence implicates gut dysbiosis in the onset and progression of neurodegenerative disorders...

Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol.

BMJ open
INTRODUCTION: To enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) al...

Critique of impure reason: Unveiling the reasoning behaviour of medical large language models.

eLife
Despite the current ubiquity of large language models (LLMs) across the medical domain, there is a surprising lack of studies which address their . We emphasise the importance of understanding as opposed to high-level prediction accuracies, since it...

Integrating machine learning and geospatial approaches for multi-hazard vulnerability mapping: implications for environmental health and contaminant risk in fragile ecosystems.

Environmental geochemistry and health
High-altitude ecosystems face growing threats from natural hazards and human activities, intensifying socio-economic and environmental risks. The Nilgiris District, Tamil Nadu, is a hotspot where steep terrain, fragile ecosystems, climate variability...

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband.

PloS one
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, ...

Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists.

Platelets
Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how plate...