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PhysioFormer: Integrating multimodal physiological signals and symbolic regression for explainable affective state prediction.

PloS one
As affective computing becomes increasingly crucial in health monitoring and psychological intervention, accurately identifying affective states is a key challenge. While traditional machine learning models have achieved some success in affective com...

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data.

PloS one
BACKGROUND AND OBJECTIVE: Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), ...

Conditional VAE for personalized neurofeedback in cognitive training.

PloS one
Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing vari...

Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010-2011 in Texas.

PloS one
BACKGROUND: Lung and Bronchus cancer is the most fatal type of cancer in the United States. According to the American Cancer Society, there were more than 127,000 deaths from lung cancer in 2023. Lung cancer care cost 23.8 billion dollars in 2020. In...

Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients with hypercholesterolemia: a machine learning study with dual-cohort validation.

Lipids in health and disease
BACKGROUND: Residual cardiovascular risk persists in patients with hypercholesterolemia despite lipid-lowering therapy, underscoring the importance of inflammation in ASCVD development. This study evaluated the relationship between Systemic Inflammat...

Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.

BMC medical imaging
PURPOSE: To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.

Understanding how medical students learn in the era of artificial intelligence: a mixed methods study.

BMC medical education
BACKGROUND: As medical education evolves, current teaching practices often remain misaligned with how today's digitally native students prefer to learn. While the use of digital tools is widespread, there is limited clarity on students' learning beha...

Multiple polygenic score approach in colorectal cancer risk prediction.

Scientific reports
Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS)...

Diagnostic assistance method for RR-TB/MDR-TB patients under treatment based on CNN-LSTM.

Scientific reports
The rapid development of deep learning has promoted its application in disease diagnosis, treatment, and prognosis prediction. Medical imaging plays a crucial role in the management of rifampicin-resistant tuberculosis/multidrug-resistant tuberculosi...

Prompt-dependent performance of multimodal AI model in oral diagnosis: a comprehensive analysis of accuracy, narrative quality, calibration, and latency versus human experts.

Scientific reports
Prompt design is a critical yet underexplored factor influencing the diagnostic performance of large language models (LLMs). Gemini Pro 2.5 shows promise in multimodal reasoning, but no prior study has systematically compared prompt structures in ora...