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Disease Progression

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Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

PloS one
Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permanent disability and stigma. Described as a disease of poverty, the impact of socioeconomic indicators ...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography ...

Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.

Arthritis research & therapy
BACKGROUND: Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study...

GDF15, EGF, and Neopterin in Assessing Progression of Pediatric Chronic Kidney Disease Using Artificial Intelligence Tools-A Pilot Study.

International journal of molecular sciences
Cell-mediated immunity and chronic inflammation are hallmarks of chronic kidney disease (CKD). Growth differentiation factor 15 (GDF15) is a marker of inflammation and an integrative signal in stress conditions. Epidermal growth factor (EGF) is a tub...

Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.

Aging clinical and experimental research
BACKGROUND: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system.

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our ...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Journal of biomedical informatics
OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subpheno...