AIMC Topic: Retrospective Studies
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment.
Frontiers in public health
Jan 7, 2025
BACKGROUND: Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and di...
Plasma Epstein-Barr Virus DNA load for diagnostic and prognostic assessment in intestinal Epstein-Barr Virus infection.
Frontiers in cellular and infection microbiology
Jan 7, 2025
BACKGROUND: The prospective application of plasma Epstein-Barr virus (EBV) DNA load as a noninvasive measure of intestinal EBV infection remains unexplored. This study aims to identify ideal threshold levels for plasma EBV DNA loads in the diagnosis ...
Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
BMC emergency medicine
Jan 6, 2025
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
Interpretable machine learning for predicting sepsis risk in emergency triage patients.
Scientific reports
Jan 6, 2025
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients.
Scientific reports
Jan 6, 2025
Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial les...
Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.
BMJ open gastroenterology
Jan 6, 2025
OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an A...
An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study.
World journal of emergency surgery : WJES
Jan 6, 2025
BACKGROUND: Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an...
Using deep learning to shorten the acquisition time of brain MRI in acute ischemic stroke: Synthetic T2W images generated from b0 images.
PloS one
Jan 6, 2025
OBJECTIVE: This study aimed to assess the feasibility of the deep learning in generating T2 weighted (T2W) images from diffusion-weighted imaging b0 images.
Development and validation of a support vector machine-based nomogram for diagnosis of obstetric antiphospholipid syndrome.
Clinica chimica acta; international journal of clinical chemistry
Jan 5, 2025
BACKGROUND: Antiphospholipid Syndrome (APS) is a systemic autoimmune disorder characterized by arterial or venous thrombosis and/or pregnancy complications. This study aims to develop a diagnostic model for Obstetric APS (OAPS) using the Support Vect...
Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.
International dental journal
Jan 5, 2025
OBJECTIVE: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.
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