Latest AI and machine learning research in information technology for healthcare professionals.
Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, its "black-box" nature prevents widespread adoption in clinical practice where transparency is essential for trust and safety. We propose ExtraCare to decompose pat...
Archaeologists, as well as specialists and practitioners in cultural heritage, require applications with additional functions, such as the annotation and attachment of metadata to specific regions of the 3D digital artifacts, to go beyond the simplistic three-dimensional (3D) visualization. Different strategies addressed this issue, most of which are excellent in their particular area of applicati...
Acquiring insights from electronic health records (EHRs) is slowed by manual analytical workflows that limit scalability and reproducibility. We prese...
Objective: Adverse events (AEs) resulting from medical interventions are significant contributors to patient morbidity, mortality, and healthcare cost...
Automatically discovering personalized sequential events from large-scale time-series data is crucial for enabling precision medicine in clinical rese...
Background: Traditional heart transplant registries often lack the granularity required for deep phenotyping and rely on labor-intensive manual abstra...
Generative models trained using self-supervision of tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction. T...
The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Ar...
Homelessness among US veterans remains a critical public health challenge, yet risk prediction offers a pathway for proactive intervention. In this re...
Background: Many patients with triple-negative breast cancer (TNBC), particularly those who are older, Black, or insured by Medicaid, do not receive g...
Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment re...
Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocar...
Objective Accurate and scalable disease phenotyping from electronic health records (EHRs) is foundational for predictive modeling and precision medici...
Foundation models trained on patient electronic health records (EHRs) hold promise for transforming clinical care by enabling effective decision suppo...
Chronic wounds affect over 1.2 million Canadians and incur healthcare costs exceeding $13 billion annually, with global expenditures approaching $149 ...
Background Patients with repaired tetralogy of Fallot (rTOF) require lifelong surveillance with cardiovascular magnetic resonance (CMR) and cardiopulm...
Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management...
Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy...
Abstract Purpose: Glaucoma, a leading cause of irreversible vision loss, often remains undiagnosed due to its asymptomatic progression and the limitat...
Purpose: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes ...