AIMC Topic: Aged, 80 and over

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A neural network approach to sarcopenia prediction based on bioelectrical impedance in community-dwelling older adults.

PloS one
This study aimed to apply a neural network to raw bioelectrical impedance analysis data and to test whether sarcopenia could be predicted with high accuracy. The study population comprised 727 community-dwelling older adults aged 65-85 years who part...

Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset.

BMC geriatrics
PURPOSE: The decline in activities of daily living (ADL) among older persons is a significant public health concern. Sarcopenia is a major risk factor for ADL disability. This study aimed to develop and validate an interpretable machine learning (IML...

Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.

Open heart
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...

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.

Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women.

Scientific reports
This study explores the use of radiomic features extracted from preoperative T2-weighted MRI and CT images, combined with machine learning models, to predict the risk of vertebral refracture after percutaneous kyphoplasty (PKP) in postmenopausal wome...

A deep learning approach to predict temporal changes of subdural hemorrhage on computed tomography.

Scientific reports
Subdural hemorrhage (SDH) is a critical condition requiring prompt assessment of its progression using computed tomography (CT). This study aimed to develop a deep-learning model to predict temporal changes in SDH by leveraging Hounsfield Units (HU) ...

Brain-derived 5-hydroxymethylcytosine epigenetic scores are related to Alzheimer's disease pathology and cognitive decline.

Acta neuropathologica
BACKGROUND: Cytosine modifications play critical roles in gene regulation and disease pathogenesis. Elucidating novel epigenetic contributions to Alzheimer's disease (AD) could advance diagnostic, prognostic, and therapeutic strategies. 5-hydroxymeth...

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.

Machine learning reveals distinct T-cell receptor clusters in plasma cell dyscrasias compared to healthy controls.

PloS one
T-cell receptor (TCR) repertoire diversity has been implicated in the progression and prognosis of multiple myeloma (MM). This study aimed to evaluate the association between T-cell clonality, immune response, and clinical outcomes in patients with p...