Latest AI and machine learning research in information technology for healthcare professionals.
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning ca...
Intelligent prediction of risk of blood transfusion among hospitalized patients can identify at-risk patients and provide timely information to the hospital to plan and reserve resources to meet the demand of blood transfusion. While previously proposed solutions focus on sub-populations such as patients admitted to ICU after gastrointestinal bleeding or postpartum patients with hemorrhage, we des...
BACKGROUND: High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured admin...
INTRODUCTION: The move from a reactive model of care which treats conditions when they arise to a proactive model which intervenes early to prevent ad...
The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency ...
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for ...
Teleoperated medical technologies are a fundamental part of the healthcare system. From telemedicine to remote surgery, they allow remote diagnosis an...
BACKGROUND: Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility ...
Clinical risk prediction with electronic health records (EHR) using machine learning has attracted lots of attentions in recent years, where one of th...
BACKGROUND AND AIMS: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient proc...
This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibratio...
The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. S...
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and ...
The increasing demand for healthcare-acquired infection (HAI) control practices and services has intensified the need to evaluate care quality. The Wo...
With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes ...
Digital health tools, platforms, and artificial intelligence- or machine learning-based clinical decision support systems are increasingly part of hea...
The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as th...
The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency...
QUESTION: Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to ...
BACKGROUND: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are...