AIMC Topic: Longitudinal Studies
Clear Filters Showing 31 to 40 of 486 articles
A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.
Journal of substance use and addiction treatment
Mar 22, 2025
BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid ...
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.
BMC pregnancy and childbirth
Mar 19, 2025
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.
BMC geriatrics
Mar 17, 2025
BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using ma...
Deep representation learning for clustering longitudinal survival data from electronic health records.
Nature communications
Mar 14, 2025
Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing ...
Development of a visualized risk prediction system for sarcopenia in older adults using machine learning: a cohort study based on CHARLS.
Frontiers in public health
Mar 12, 2025
INTRODUCTION: The older adult are at high risk of sarcopenia, making early identification and scientific intervention crucial for healthy aging.
Individual and integrated indexes of inflammation predicting the risks of mental disorders - statistical analysis and artificial neural network.
BMC psychiatry
Mar 11, 2025
OBJECTIVE: The prevalence of mental illness in Taiwan increased. Identifying and mitigating risk factors for mental illness is essential. Inflammation may be a risk factor for mental illness; however, the predictive power of inflammation test values ...
An explainable machine learning-based prediction model for sarcopenia in elderly Chinese people with knee osteoarthritis.
Aging clinical and experimental research
Mar 7, 2025
BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disease that leads to loss of muscle mass and function, resulting in adverse health outcomes such as falls, functional decline, and death. Knee osteoarthritis (KOA) is a common chro...
Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.
Cell reports. Medicine
Mar 4, 2025
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability...
Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.
BMJ health & care informatics
Feb 27, 2025
OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patients in real time using effluent-measured biomarkers of the mesothelial-to-mesenchymal transition (MMT)...
Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.
BMC geriatrics
Feb 27, 2025
OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.
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