AIMC Topic: Disease Progression
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Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.
Nature
Apr 16, 2025
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood. We use spatial proteomics by mass spectrometry and machine learning to map AATD in h...
Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease.
Renal failure
Apr 15, 2025
OBJECTIVE: Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model...
Predicting diabetes mellitus metabolic goals and chronic complications transitions-analysis based on natural language processing and machine learning models.
PloS one
Apr 15, 2025
OBJECTIVE: To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic targets and development of complications.
Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.
Scientific reports
Apr 11, 2025
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 ...
Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.
Journal of affective disorders
Apr 10, 2025
BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. This study systematically analyzed data from 2019 to 2023 to explore trends in MDD incidence, symptom...
A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease.
Annals of epidemiology
Apr 8, 2025
PURPOSE: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determined by two endogenous markers measured in blood: serum creatine (SCr) and cystatin C (CysC). Machine ...
Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.
IEEE transactions on neural networks and learning systems
Apr 4, 2025
Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have imple...
Effects of Renal Function on the Multimodal Brain Networks Affecting Mild Cognitive Impairment Converters in End-Stage Renal Disease.
Academic radiology
Apr 3, 2025
RATIONALE AND OBJECTIVES: Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transi...
Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.
Journal of biomedical informatics
Apr 1, 2025
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...
Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression: A Randomized Trial.
JAMA network open
Apr 1, 2025
IMPORTANCE: Historically, fewer than 10% of adults with cancer have enrolled in clinical trials. Computational tools have been developed to match patients to trials, but these tools are relevant only when patients need new treatment.
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