Acute Coronary Syndrome

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.

INTRODUCTION: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performa...

Oral anticoagulant treatment in atrial fibrillation: the AFIRMA real-world study using natural language processing and machine learning.

INTRODUCTION: Oral anticoagulation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain lacks substantial real-world evidence. We ai...

Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mains...

Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes.

BACKGROUND: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of ...

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical re...

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization.

Safety and Efficacy of Acute Central Venous Catheters for Hemodialysis with Sodium Bicarbonate versus an Antibiotic Catheter Locking Solution.

This study was conducted to determine the safety and efficacy of acute central venous catheters (CVC) using a sodium bicarbonate catheter locking solu...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use ...

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data...

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP.

OBJECTIVE: Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with impro...

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

BACKGROUND: The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learnin...

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline pre...

Thrombosed Persistent Median Artery with Coexisting Bifid Median Nerve in a Robotic Arthroplasty Surgeon: A Case Report.

CASE: A 47-year-old orthopaedic surgeon presented with acute volar left wrist pain. He performed over 250 robot-assisted knee arthroplasties each year...

On the relationship between various anticoagulants and robot-assisted radical prostatectomy: a single-surgeon serial analysis.

Prostate cancer patients often have other health conditions and take anticoagulants. It was believed that surgery under anticoagulants could worsen su...

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model.

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. E...

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review.

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and h...

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. ...

1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.

: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity a...

Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (A...