Geriatrics

Machine learning using random forest to differentiate between blow and fall situations of head trauma.

Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, particularly when it comes to distinguishing between falls or inflicted blows. The objective of this study was to develop a predictive model using an innovative Random Forest (RF) classification approach to differentiate injuries caused by falls from those...

From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.

With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative breast cancer. Concurrently, the significant therapeutic effect of T-DXd on patients with HER2-low tumors has brought this group into the public spotlig...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop ...

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.

Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurat...

Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults.

This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (T) and skin (T) temperatures of ol...

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects m...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and...

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic ...

Transforming residential water end use analysis: unleashing insights from widespread low-resolution smart metering data.

Transforming smart meter data captured at low-resolution litre intervals of 15 to 60 mins into residential water end use data provides valuable insigh...

Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.

Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevan...

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

BACKGROUND: Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no stand...

Long-tailed medical diagnosis with relation-aware representation learning and iterative classifier calibration.

Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sa...

Characterizing Brain-Cardiovascular Aging Using Multiorgan Imaging and Machine Learning.

The structure and function of the brain and cardiovascular system change over the lifespan. In this study, we aim to establish the extent to which age...

Towards realistic simulation of disease progression in the visual cortex with CNNs.

Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these paral...

Accelerating veterinary low field MRI acquisitions using the deep learning based denoising solution HawkAI.

Magnetic resonance imaging (MRI) has changed veterinary diagnosis but its long-sequence time can be problematic, especially because animals need to be...

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD)...

Artificial Intelligence in Commercial Industry: Serving the End-to-End Patient Experience Across the Digital Ecosystem.

The purpose of this article is to evaluate the application of artificial intelligence (AI) from the perspective of the orthopaedic industry with respe...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations...

Dense convolution-based attention network for Alzheimer's disease classification.

Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize perform...

Cultural variation in trust and acceptability of artificial intelligence diagnostics for dementia.

Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for underrepresented groups. We explored perceptions o...