Acute Coronary Syndrome

[Artificial intelligence-based automated assessment of coronary flow reserve from angiography and the impact of different vasodilators].

To explore the feasibility of a coronary angiography-based method developed with artificial intelli...

Pre-hospital delay and mortality in different age groups with acute coronary syndrome: do we have enough evidence?

INTRODUCTION: Pre-hospital delay (p-HD) in acute coronary syndrome (ACS) influences the ability to p...

Novel AI Guided Non-Expert Compression Ultrasound DVT Diagnostic Pathway May Reduce Vascular Laboratory Venous Testing .

OBJECTIVE: Ultrasonography and D-dimer testing are established modalities for evaluating potential l...

Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective.

: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, part...

Acute coronary syndromes: mechanisms, challenges, and new opportunities.

Despite advances in research and patient management, atherosclerosis and its dreaded acute and chron...

ACS NSQIP Risk Calculator Performance Across Multiple Domains of Operative Risk and Risk-associated Features.

OBJECTIVE: To assess the accuracy of the ACS NSQIP Risk Calculator (RC) when applied to subsets of h...

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations.

Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic di...

[Pulmonary vascular interventions: innovating through adaptation and advancing through differentiation].

Pulmonary vascular intervention technology, with its minimally invasive and precise advantages, has ...

Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.

BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory sys...

Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning.

PURPOSE: To develop machine learning models using the American College of Surgeons National Quality ...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, rai...

Association of early enoxaparin prophylactic anticoagulation with ICU mortality in critically ill patients with chronic obstructive pulmonary disease: a machine learning-based retrospective cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major contributor to global morbidity ...

Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

BACKGROUND: The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) ...

Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome.

Neuropathologic changes of Alzheimer disease (AD) including Aβ accumulation and neuroinflammation ar...

Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction.

Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units.

Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic ...

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the ...

Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.

OBJECTIVE: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is e...

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE...

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual ...