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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...

Barriers and enablers for generative artificial intelligence in clinical psychology: a qualitative study based on the COM-B and theoretical domains framework (TDF) models.

BMC psychology
BACKGROUND: This study investigated the perceptions of care psychologists regarding the adoption of generative artificial intelligence (GenAI) in therapeutic practice. As AI continues to be integrated into various sectors, including healthcare, under...

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.

BMC research notes
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

Can machine learning improve on the early prediction of upper limb recovery after stroke?

Journal of neuroengineering and rehabilitation
BACKGROUND: Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predi...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

Academic misconduct and artificial intelligence use by medical students, interns and PhD students in Ukraine: a cross-sectional study.

BMC medical education
BACKGROUND: The issues regarding the use of artificial intelligence (AI) and academic integrity are important contemporary topics. There are no clear regulations governing the use of AI in academic institutions in Ukraine. This study aimed to explore...

Integrating deep learning and multi-omics features in radiation pneumonitis prediction for lung cancer patients using PET/CT.

BMC medical imaging
BACKGROUND: To investigate the feasibility and accuracy of PET radiomics features, along with their combination with CT radiomics, dosiomics, and deep learning (DL) features, in predicting radiation pneumonitis (RP) in lung cancer patients treated wi...

The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.

BMC cancer
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.

The effect of kinesiophobia and successful aging on quality of life in older adults: machine learning approach.

BMC geriatrics
BACKGROUND: Kinesiophobia and successful aging are key factors affecting quality of life in older adults; kinesiophobia, the fear of movement, can lead to reduced physical activity, while successful aging promotes overall well-being.