AIMC Topic: Middle Aged

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Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

BMC infectious diseases
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Automatic development of speech-in-noise hearing tests using machine learning.

Scientific reports
Understanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are ...

Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.

European journal of medical research
OBJECTIVES: This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms.

Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.

Frontiers in immunology
INTRODUCTION: Aging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).

Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity.

Frontiers in cellular and infection microbiology
OBJECTIVE: Metagenomic next-generation sequencing (mNGS) can potentially detect various pathogenic microorganisms without bias to improve the diagnostic rate of fever of unknown origin (FUO), but there are no effective methods to predict mNGS-positiv...

The spatiotemporal ecology of Oropouche virus across Latin America: a multidisciplinary, laboratory-based, modelling study.

The Lancet. Infectious diseases
BACKGROUND: Latin America has been experiencing an Oropouche virus (OROV) outbreak of unprecedented magnitude and spread since 2023-24 for unknown reasons. We aimed to identify risk predictors of and areas at risk for OROV transmission.

Population-level individualized prospective prediction of opioid overdose using machine learning.

Molecular psychiatry
The opioid overdose epidemic has rapidly expanded in North America, with rates accelerating during the COVID-19 pandemic. No existing study has demonstrated prospective opioid overdose at a population level. This study aimed to develop and validate a...