AIMC Topic: Disease Progression
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Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.
ESC heart failure
May 13, 2024
AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared wit...
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.
Journal of medical systems
May 13, 2024
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in...
Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.
Journal of the neurological sciences
May 12, 2024
INTRODUCTION: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH usin...
Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.
The American journal of cardiology
May 6, 2024
Patients with moderate aortic stenosis (AS) have a greater risk of adverse clinical outcomes than that of the general population. How this risk compares with those with severe AS, along with factors associated with outcomes and disease progression, i...
Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.
American journal of ophthalmology
May 3, 2024
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
Retinal Ganglion Cells
Visual Fields
Male
Optic Nerve Diseases
Retrospective Studies
Aging
Glaucoma, Open-Angle
Middle Aged
Female
Disease Progression
Aged
Tomography, Optical Coherence
Intraocular Pressure
Deep Learning
Nerve Fibers
Humans
Follow-Up Studies
Optic Disk
Neural Networks, Computer
Glaucoma
Algorithms
Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2024
Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and im...
Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.
Journal of magnetic resonance imaging : JMRI
Apr 30, 2024
BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.
Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.
Cell reports. Medicine
Apr 26, 2024
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of mach...
Predicting chronic kidney disease progression with artificial intelligence.
BMC nephrology
Apr 26, 2024
BACKGROUND: The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validat...
Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.
Diabetes technology & therapeutics
Apr 24, 2024
To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit...
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