AIMC Topic: Longitudinal Studies
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Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PloS one
Jan 24, 2025
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...
Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.
BMC cancer
Jan 22, 2025
BACKGROUND: Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitat...
AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice.
European journal of radiology
Jan 18, 2025
OBJECTIVES: In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of ...
Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.
Clinical and experimental nephrology
Jan 15, 2025
BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinica...
LMSST-GCN: Longitudinal MRI sub-structural texture guided graph convolution network for improved progression prediction of knee osteoarthritis.
Computer methods and programs in biomedicine
Jan 13, 2025
BACKGROUND AND OBJECTIVES: Accurate prediction of progression in knee osteoarthritis (KOA) is significant for early personalized intervention. Previous methods commonly focused on quantifying features from a specific sub-structure imaged at baseline ...
A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.
BMC geriatrics
Jan 11, 2025
OBJECTIVE: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.
Translational psychiatry
Jan 11, 2025
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functio...
Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.
American journal of veterinary research
Jan 9, 2025
OBJECTIVE: To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRRS) virus and the basic demographic data on the severity of the impact on selected production paramet...
A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.
The journal of headache and pain
Jan 9, 2025
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance...
Longitudinal twin growth discordance patterns and adverse perinatal outcomes.
American journal of obstetrics and gynecology
Jan 7, 2025
BACKGROUND: Growth discordance in twin pregnancies is associated with increased perinatal morbidity and mortality, yet the patterns of discordance progression and the utility of Doppler assessments remain underinvestigated.
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