AIMC Topic: Longitudinal Studies

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VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately predicting the progression trend of MCI is critical to the early prevention and treatment of AD. Brain...

Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System.

Journal of the American College of Surgeons
BACKGROUND: In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality...

Urologic latency time during uroflow stop test with electromyography: an incontinence detector in rehabilitation after robotic radical prostatectomy.

European journal of physical and rehabilitation medicine
BACKGROUND: Stress urinary incontinence (UI) is the most common presentation following robot-assisted radical prostatectomy (RARP), but a postoperative non-invasive and objective test is still lacking. To assess pelvic floor integrity after RARP, we ...

A validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening.

The Journal of allergy and clinical immunology
BACKGROUND: Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptim...

Smart home technology to support older people's quality of life: A longitudinal pilot study.

International journal of older people nursing
AIM: This pilot study aimed to explore the impact of Smart Home technology to support older people's quality of life, particularly for those who live alone.

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Cells
Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for ...

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Scientific reports
Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease. Knowing from previous studies that these two factors can manifest in the retina, the aim was to investigate whether a deep learning metho...

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Scientific reports
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding...

An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

Journal of general internal medicine
BACKGROUND: Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there...

Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...