AIMC Topic: Adult

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Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.

Nature communications
Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease with a median survival of only 2 to 4 years from diagnosis. Improved tools are needed to shorten diagnostic delays and improve prognostication to benefit clinical care....

Development and application of a deep learning-based tuberculosis diagnostic assistance system in remote areas of Northwest China.

Scientific reports
The Kashgar region, located in Northwest China, has a significantly higher incidence of tuberculosis (TB) compared to the national average. Local governments conduct annual TB screening using medical imaging. However, due to a shortage of radiologist...

Machine learning methods on BioVid heat pain database for pain intensity estimation.

Scientific reports
Pain assessment is a critical aspect of medical practice, directly influencing patient treatment and quality of life. Traditional pain evaluation methods, such as the Numerical Rating Scale (NRS), Visual Analog Scale (VAS), and Verbal Rating Scale (V...

An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.

Translational psychiatry
Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...

Optic disc morphometrics as a potential ocular biomarker for depression: evidence from two cross-sectional cohort studies.

Translational psychiatry
Depression, which is increasingly prevalent among older adults, has traditionally been diagnosed through symptom-based questionnaires. However, emerging evidence suggests that retinal changes could serve as objective biomarkers for depression. In thi...

The application of amplitude of low-frequency fluctuations metrics in the diagnosis and prediction of treatment response as well as their associated genes and biological processes in patients with bipolar disorder.

Translational psychiatry
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...

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...

Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

JMIR formative research
BACKGROUND: Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research h...

Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.

Clinical and experimental medicine
OBJECTIVE: To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabeti...

A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.

PloS one
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...