OBJECTIVE: Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines discourage ongoing access salvage attempts after two interventions prior to successful use or more than three interventions per year overall. The goal was to develop a tool for ...
ASAIO journal (American Society for Artificial Internal Organs : 1992)
Dec 21, 2023
This study presents Neuro-SPARK, the first scoring system developed to assess the risk of neurologic injury in pediatric and neonatal patients on extracorporeal membrane oxygenation (ECMO). Using the extracorporeal life support organization (ELSO) re...
OBJECTIVES: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.
With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identifi...
BACKGROUND: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artifici...
Environmental pollution (Barking, Essex : 1987)
Dec 8, 2023
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluat...
BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease.
BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to progno...
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...
BACKGROUND: Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create dig...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.