AIMC Topic: Severity of Illness Index
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The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.
Depression and anxiety
Apr 10, 2025
The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at pr...
Application of machine learning in assessing disease activity in SLE.
Lupus science & medicine
Apr 8, 2025
OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to const...
Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.
European journal of pediatrics
Apr 7, 2025
UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...
Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.
Clinical and translational gastroenterology
Apr 1, 2025
INTRODUCTION: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our a...
A deep learning approach to remotely assessing essential tremor with handwritten images.
Scientific reports
Mar 28, 2025
Essential tremor (ET) is the most prevalent movement disorder, with its incidence increasing with age, significantly impacting motor functions and quality of life. Traditional methods for assessing ET severity are often time-consuming, subjective, an...
Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation.
JACC. Cardiovascular imaging
Mar 26, 2025
BACKGROUND: Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically.
Aortic Valve Insufficiency
Magnetic Resonance Imaging, Cine
United States
Aortic Valve
Unsupervised Machine Learning
Humans
Predictive Value of Tests
Machine Learning
Sex Factors
Risk Assessment
Decision Support Techniques
Disease Progression
Aged
Phenotype
Severity of Illness Index
Prognosis
Adult
Time Factors
Male
Retrospective Studies
Ventricular Function, Left
Female
Middle Aged
Reproducibility of Results
Risk Factors
Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.
Scientific reports
Mar 26, 2025
COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like intensive care unit beds and ventilation equipment. Many prognostic statistical tools have been devel...
Machine learning-based model for predicting all-cause mortality in severe pneumonia.
BMJ open respiratory research
Mar 22, 2025
BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians i...
Measurement of adipose body composition using an artificial intelligence-based CT Protocol and its association with severe acute pancreatitis in hospitalized patients.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Mar 21, 2025
BACKGROUND/OBJECTIVES: The clinical utility of body composition in predicting the severity of acute pancreatitis (AP) remains unclear. We aimed to measure body composition using artificial intelligence (AI) to predict severe AP in hospitalized patien...
Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.
The Journal of dermatological treatment
Mar 19, 2025
BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.
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