AIMC Topic: Longitudinal Studies
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Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.
Scientific reports
Jul 1, 2025
Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal clinical records. The motivation stems from the need for timely medical resource allocation, improved ...
Evolving Perceptions and Attitudes to Adopting Generative AI in Professional Settings: Multicenter Longitudinal Qualitative Study of Senior Chinese Hospital Leaders.
Journal of medical Internet research
Jun 27, 2025
BACKGROUND: The rapid evolution of generative artificial intelligence (GenAI) is transforming health care globally. In China, hospitals are rapidly embracing digital transformation. Senior leaders are pivotal in influencing and deciding the adoption ...
Progression and natural history of Atypical Parkinsonism (ATPARK): Protocol for a longitudinal follow-up study from an underrepresented population.
PloS one
Jun 26, 2025
BACKGROUND: Atypical Parkinsonian Syndromes (APS) form the third largest group of neurodegenerative disorders including Progressive Supranuclear Palsy (PSP), Multiple System Atrophy (MSA), and Corticobasal Syndrome (CBS). These conditions are charact...
Artificial intelligence and the wellbeing of workers.
Scientific reports
Jun 23, 2025
This study explores the relationship between artificial intelligence (AI) and workers' well-being and health using longitudinal survey data from Germany (2000-2020). Using a measure of occupational exposure to AI, we explore an event study design and...
Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.
PloS one
Jun 17, 2025
BACKGROUND: Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and its determi...
Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.
BMC medical informatics and decision making
Jun 6, 2025
The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML) technology has substantial potential to disease prediction. The present study use dataset from the ...
Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease.
PloS one
Jun 5, 2025
BACKGROUND: The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.
Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.
Journal of health, population, and nutrition
Jun 4, 2025
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...
Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.
JMIR formative research
Jun 3, 2025
BACKGROUND: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% ...
Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.
BMC public health
Jun 2, 2025
BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identification is essential for delaying disease progression.
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