Acute Coronary Syndrome

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis.

Recently, an emerging trend in medical image classification is to combine radiomics framework with d...

An artificial neural network approach integrating plasma proteomics and genetic data identifies PLXNA4 as a new susceptibility locus for pulmonary embolism.

Venous thromboembolism is the third common cardiovascular disease and is composed of two entities, d...

Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study.

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is ...

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among ...

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

Machine learning (ML) has been suggested to improve the performance of prediction models. Neverthele...

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.

OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for time...

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.

OBJECTIVE: Some researchers have studied about early prediction and diagnosis of major adverse cardi...

Systematic review of machine learning models for personalised dosing of heparin.

AIM: To identify and critically appraise studies of prediction models, developed using machine learn...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosi...

In Vitro Measurements of Shear-Mediated Platelet Adhesion Kinematics as Analyzed through Machine Learning.

Platelet adhesion to blood vessel walls in shear flow is essential to initiating the blood coagulati...

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased c...

Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.

Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very...

Robot-assisted transthoracic first rib resection for venous thoracic outlet syndrome.

BACKGROUND: Venous thoracic outlet syndrome (vTOS) is caused by external compression of the subclavi...

Prediction of activity cliffs on the basis of images using convolutional neural networks.

An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference...

Assessment of Collaborative Robot (Cobot)-Assisted Histotripsy for Venous Clot Ablation.

OBJECTIVE: The application of bubble-based ablation with the focus ultrasound therapy histotripsy is...

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals.

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of rela...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders,...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Modern motor imagery (MI)-based brain computer interface systems often entail a large number of elec...

Comment and Update on "Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management".

Response: Comment and Update on "Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management".

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detec...