AI Medical Compendium Topic

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Disease Progression

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Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management.

Scientific reports
High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitating advanced diagnostic and prognostic strategies. This systematic review and epistemic meta-analysis...

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

Nutrients
: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM ...

Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

World journal of gastroenterology
BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

Journal of translational medicine
BACKGROUND: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD i...

Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression: A Randomized Trial.

JAMA network open
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.

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great poten...

Machine learning predictions of tumor progression: How reliable are we?

Computers in biology and medicine
BACKGROUND: Cancer continues to pose significant challenges in healthcare due to the complex nature of tumor progression. In this digital era, artificial intelligence has emerged as a powerful tool that can potentially transform multiple aspects of c...

Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease.

Renal failure
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...

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
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...