AIMC Topic: Longitudinal Studies
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Late Life Cognitive Function Trajectory Among the Chinese Oldest-Old Population-A Machine Learning Approach.
Journal of gerontological social work
Apr 8, 2024
Informed by the biopsychosocial framework, our study uses the Chinese Longitudinal Healthy Longevity Survey (CLHLS) dataset to examine cognitive function trajectories among the oldest-old (80+). Employing K-means clustering, we identified two latent ...
Machine Learning Approach to Study Social Determinants of Chronic Illness in India: A Comparative Analysis.
Indian journal of public health
Apr 4, 2024
BACKGROUND: Several studies on noncommunicable diseases (NCDs) have been carried out worldwide, the basis of most of which is the identification of risk factors-modifiable (or behavioral) and metabolic. Majority of the NCDs are due to sociodemographi...
Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.
Journal of applied gerontology : the official journal of the Southern Gerontological Society
Mar 31, 2024
This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) ...
A Longitudinal Analysis of Pre- and Post-Operative Dysmorphology in Metopic Craniosynostosis.
The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association
Mar 14, 2024
OBJECTIVE: The purpose of this study is to objectively quantify the degree of overcorrection in our current practice and to evaluate longitudinal morphological changes using CranioRate, a novel machine learning skull morphology assessment tool.  Desi...
Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.
Journal of sport and health science
Feb 29, 2024
BACKGROUND: There exist few maximal oxygen uptake (VO) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO is infeasible in large epidemiologic cohort stud...
Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.
Annals of clinical and translational neurology
Feb 23, 2024
OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index ...
Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study.
Journal of affective disorders
Feb 20, 2024
BACKGROUND: Machine learning (ML) has been widely used to predict suicidal ideation (SI) in adolescents and adults. Nevertheless, studies of accurate and efficient models of SI prediction with preadolescent children are still needed because SI is sur...
Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study.
Nutrition, metabolism, and cardiovascular diseases : NMCD
Feb 15, 2024
BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate pot...
Predicting Homelessness Among Transitioning U.S. Army Soldiers.
American journal of preventive medicine
Feb 3, 2024
INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.
Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.
Magma (New York, N.Y.)
Feb 1, 2024
OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with o...
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