Acute Coronary Syndrome

Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.

BACKGROUND: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking. OBJECTIVES: To train and validate an efficient NLP model to detect incident VTE event. METHODS: We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data prepr...

A machine learning-driven transcriptomic study reveals the key role of Romo1 in reversing central sensitization through stellate ganglion block in migraine: An interventional study based on a recurrent migraine rat model.

OBJECTIVES/BACKGROUND: This study utilized a nitroglycerin (NTG)-induced chronic migraine rat model to explore the therapeutic effects and underlying mechanisms of stellate ganglion block (SGB), aiming to clarify the potential pathogenesis of migraine. Central sensitization is key to chronic migraine development. SGB effectively treats chronic pain by reducing sympathetic tone and inhibiting centr...

Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.

Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessm...

Molecular property prediction in the ultra-low data regime.

Data scarcity remains a major obstacle to effective machine learning in molecular property prediction and design, affecting diverse domains such as ph...

Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.

BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular condition mainly affecting individuals aged 75 and a...

A modified generative adversarial networks method for assisting the diagnosis of deep venous thrombosis complications in stroke patients.

Discriminate deep vein thrombosis, one of the complications in early stroke patients, in order to assist in diagnosis. We have constructed a new metho...

Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.

BACKGROUND: T2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, ...

Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024.

BACKGROUND: Thrombosis of arteriovenous fistulas represents a prevalent complication among patients undergoing hemodialysis, characterized by a notabl...

Method for Predict Stenosis of Arteriovenous Fistula Patients Based on Machine Learning.

OBJECTIVES: Arteriovenous fistula (AVF) is the most ideal vascular access for hemodialysis. People with AVF have a longer vascular access survival rat...

Validation of the ACS-NSQIP surgical risk calculator for patients with paraoesophageal hernias undergoing robotic repair.

BACKGROUND: The National Surgical Quality Improvement Program (NSQIP) American College of Surgeons (ACS) risk calculator is a validated method of pred...

Thrombo-vera: a new thrombosis risk model for polycythemia vera using modern variable selection methods.

BACKGROUND: Thrombosis is a major complication in polycythemia vera (PV), contributing to significant morbidity and mortality. This retrospective stud...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased risk of systemic thromboembolism and stroke. Anti...

Accelerated MRI in temporomandibular joints using AI-assisted compressed sensing technique: a feasibility study.

OBJECTIVE: To investigate the feasibility of accelerated MRI with artificial intelligence-assisted compressed sensing (ACS) technique in the temporoma...

Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.

OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary ...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) scor...

Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19.

Chronic kidney disease (CKD) patients with coronavirus disease 2019 (COVID-19) are at significant risk of death. However, clinical identification of ...

A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.

BACKGROUND & AIMS: Deep vein thrombosis, a common complication after laparoscopic surgery, can negatively affect patients' limb motor function and eve...

Performance of convolutional neural network-enhanced electrocardiography in detecting acute coronary syndrome: focusing on subtypes and reduced leads.

BACKGROUND: Early and accurate diagnosis of acute coronary syndrome (ACS), particularly non-ST-elevation ACS (NSTE-ACS), remains a critical challenge ...

Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score.

Our study aims to improve the prediction performance of machine learning (ML) models by addressing false records (i.e., false positive, false negative...

Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models.

Motivated by the need to enhance the accuracy of forest aboveground carbon storage (ACS) assessments, this study aimed to explore the effectiveness of...