AIMC Topic: Longitudinal Studies
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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.
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.
Scientific reports
Jan 7, 2025
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims...
Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study.
BMJ open
Jan 7, 2025
INTRODUCTION: Heart failure (HF) is a complex clinical syndrome. Accurate risk stratification and early diagnosis of HF are challenging as its signs and symptoms are non-specific. We propose to address this global challenge by developing the STRATIFY...
Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic ...
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.
Frontiers in public health
Jan 7, 2025
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.
A Longitudinal Prediction of Suicide Attempts in Borderline Personality Disorder: A Machine Learning Study.
Journal of clinical psychology
Jan 3, 2025
Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable patients in clinical practice remains a challenge so far. The current study aimed at predicting suicide ...
Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.
IEEE transactions on medical imaging
Jan 2, 2025
The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these traject...
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