Acute Coronary Syndrome

Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.

Left ventricular thrombus (LVT) in ischemic heart failure carries embolic risk; tools to anticipate persistence are limited. We studied 190 consecutive patients with imaging-confirmed LVT managed with guideline-concordant anticoagulation and serial echocardiography. The primary outcome was 6-month non-regression; 1-year MACE was secondary. We combined classical statistics with explainable machine ...

Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.

BACKGROUND: Heart failure (HF) readmissions remain common and costly, yet existing prediction models show limited clinical utility, particularly at the time of emergency department (ED) presentation. OBJECTIVES: This study aimed to develop and evaluate machine learning (ML) models to predict 12-month HF readmission among patients presenting to EDs with suspected cardiac conditions. METHODS: This w...

Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review.

Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threa...

What the electrocardiogram is being asked to detect in acute myocardial infarction: The case for the OMI/NOMI paradigm.

The electrocardiogram is the pivotal triage instrument in suspected acute myocardial infarction, yet for three decades its interpretation has been red...

Gut microbiome signatures discriminate deep vein thrombosis through machine learning and metabolic analysis.

Deep vein thrombosis (DVT) remains difficult to distinguish because of its often silent presentation and the limited specificity of current diagnostic...

From biomimicry to clinical actionability: rethinking high-shear thrombosis as a mechanobiological system.

PURPOSE OF REVIEW: Arterial thrombosis remains a leading cause of morbidity and mortality worldwide, while its mechanistic understanding and clinical ...

Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

IMPORTANCE: Glossectomy and reconstruction for tongue tumors carries substantial risk of postoperative morbidity, yet current tools offer limited indi...

High-throughput signal detection of hepatotoxic drug-drug interactions in hospitalized elderly patients: an NLP-driven pharmacovigilance study.

BACKGROUND: Elderly patients are highly susceptible to drug-drug interaction (DDI)-induced liver injury, yet comprehensive real-world evidence remains...

Grading knee cartilage degeneration and meniscal injury with combined ACS-accelerated T1ρ and T2 mapping.

OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence-compressed sensing (ACS)-accelerated T1ρ mapping combined with convention...

Applying machine learning to pharmacovigilance data: A proof-of-concept study.

AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance d...

Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.

Thrombosis remains a major cause of morbidity and mortality in patients with cancer. Existing risk models fail to reliably predict venous thromboembol...

Multimodal Deep Learning for Predicting Deep Vein Thrombosis Risk After Total Knee Arthroplasty: A Clinical Study.

BACKGROUND: Deep vein thrombosis (DVT) following total knee arthroplasty (TKA) remains a major postoperative complication. Conventional risk assessmen...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

Local infection signs at the catheter-insertion site: a large prospective multicentric cohort study.

OBJECTIVES: To describe the epidemiology of local signs at intravascular catheter insertion sites across catheter types and care settings, to compare ...

drGT: Interpretable Drug Response Prediction with Attention-Guided Gene Attribution on a Drug-Cell-Gene Heterogeneous Graph.

BACKGROUND: For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present...

Proteomics as a Theranostic Compass in BCR::ABL1-Negative Myeloproliferative Neoplasms: Integrating Biomarker Discovery with Therapeutic Stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal ha...

A qualitative interview study of acute care surgeons' approach to literature acquisition: Are you up to date on staying up to date?

INTRODUCTION: Acute Care Surgery (ACS) surgeons care for a wide variety of life-threatening surgical conditions and have a high burden of night and we...

Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.

AIMS: Perioperative myocardial injury (PMI) is a frequent and often asymptomatic complication after non-cardiac surgery and is associated with increas...

Construction of a prediction model for isolated distal deep vein thrombosis after total knee arthroplasty based on machine learning.

BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is a common complication after arthroplasty. In this study, we calculated the incidence of ID...

Decoding Herbal Medicine: Machine Learning-Driven Insights into Structural Identification and Pharmacological Research.

Herbal medicine is indispensable and crucial to many healthcare systems. Its therapeutic value arises from diverse plant-derived compounds with potent...