AIMC Topic: Registries
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Deep Learning-Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.
Circulation. Cardiovascular imaging
Jan 21, 2025
BACKGROUND: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricusp...
Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.
Clinical and experimental nephrology
Jan 15, 2025
BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinica...
Adverse Outcomes after Cemented and Cementless Primary Elective Total Hip Arthroplasty in 60,064 Matched Patients: A Study of Data from the Swedish Arthroplasty Register.
The Journal of arthroplasty
Jan 13, 2025
BACKGROUND: The choice between cemented and cementless fixation in primary elective total hip arthroplasty (THA) remains a subject of ongoing debate. However, comparisons between the two are subject to limited adjustments for patient characteristics,...
A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.
NeuroImage. Clinical
Jan 12, 2025
BACKGROUND: Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-resonance-imaging commonly fail to detect hyperacute POCI. Studies suggest expert inspection of Computed ...
ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.
JCO clinical cancer informatics
Jan 9, 2025
PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based ...
Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
PloS one
Jan 7, 2025
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 20...
The use of machine learning for the prediction of response to follow-up in spine registries.
International journal of medical informatics
Jan 3, 2025
BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the cha...
Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.
Medicina (Kaunas, Lithuania)
Dec 27, 2024
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...
Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies.
RMD open
Dec 20, 2024
OBJECTIVES: We propose and test a framework to detect disease diagnosis using a recent large language model (LLM), Meta's Llama-3-8B, on French-language electronic health record (EHR) documents. Specifically, it focuses on detecting gout ('goutte' in...
Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.
BMC cardiovascular disorders
Dec 19, 2024
BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machi...
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