Acute Coronary Syndrome

Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.

Recently, machine learning (ML) has gained popularity in the early stages of drug discovery. This trend is unsurprising given the increasing volume of relevant experimental data and the continuous improvement of ML algorithms. However, conventional models, which rely on the principle of molecular similarity, often fail to capture the complexities of chemical interactions, particularly those involv...

Bleeding risk assessment tools in patients with atrial fibrillation taking anticoagulants: a comparative review and clinical implications.

INTRODUCTION: Bleeding risk assessment plays a critical role in the anticoagulation management for atrial fibrillation (AF), to balance stroke prevention with risk of major hemorrhage. Traditional bleeding risk models, such as HAS-BLED, ORBIT, and ATRIA, offer valuable insights but have limitations in predictive accuracy and clinical applicability. Recent advances in risk stratification have intro...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and m...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular dr...

Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS...

Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artificial intelligence. (RAPIDx AI)-a cluster randomized study design.

BACKGROUND: Clinical work-up for suspected cardiac chest pain is resource intensive. Despite expectations, high-sensitivity cardiac troponin assays ha...

Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections

Purpose: Aortic dissections are life-threatening cardiovascular conditions requiring accurate segmentation of true lumen (TL), false lumen (FL), and...

Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modelling of molecular crystals. H...

A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing

Adaptive Curriculum Sequencing (ACS) is essential for personalized online learning, yet current approaches struggle to balance complex educational c...

SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models

Despite advances in deep learning for automatic sleep staging, clinical adoption remains limited due to challenges in fair model evaluation, general...

Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking.

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscop...

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality,...

[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 intelligence which was able to automatically and quickly ...

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 perform percutaneous coronary intervention in a tim...

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 lower extremity deep venous thrombosis (DVT). The T...

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 high-risk patients.

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

Despite advances in research and patient management, atherosclerosis and its dreaded acute and chronic sequelae continue to account for one out of thr...

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, particularly in high-risk patients undergoing coronary...

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

Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostat...

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 diseases. Nipah virus (NiV) of Paramyxoviridae famil...