Acute Coronary Syndrome

AI-MI: A Deep Learning Model to Predict Actionable Acute Coronary Syndrome Using 12-Lead ECGs

Chest pain is among the most common chief complaints in Emergency Departments (EDs), and differentia...

AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks

Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (...

Advancing In-Hospital Mortality Prediction for Acute Myocardial Infarction: an analysis from the American Heart Association Get-With-the-Guidelines Coronary Artery Disease Registry

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with acute myocardial...

Risk Prediction Modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: Leveraging Machine Learning

Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary interventio...

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challe...

Two-Stage Machine Learning Based Prediction of Thrombophilia Management

Thrombophilia diagnosis and management rely on the nuanced interpretation of clinical history, risk ...

Artificial intelligence-driven ECG biomarkers for screening of large pericardial effusion

Pericardial effusion can progress to life-threatening cardiac tamponade when large or rapidly accumu...

Machine learning model predicts new-onset lower extremity deep vein thrombosis after pelvic fracture surgery and targeted diagnosis

Postoperative new-onset deep vein thrombosis (PNO-DVT) of the lower extremities represents a prevale...

A Wearable Infrared Sensor for Detecting Non-ST Segment Elevation Acute Coronary Syndromes

Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is conventionally diagnosed using electr...

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 ...

A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients

Early detection of surgical complications allows for timely therapy and proactive risk mitigation....

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...

Regional End-Systolic Circumferential Strain Demonstrates Reduced Function in Remote Myocardium after Anterior STEMI

Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remo...

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 ...

Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thromb...

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 ...