Studies in health technology and informatics
Jan 25, 2024
Clinical text contains rich patient information and has attracted much research interest in applying Natural Language Processing (NLP) tools to model it. In this study, we quantified and analyzed the textual characteristics of five common clinical no...
In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature a...
Journal of X-ray science and technology
Jan 1, 2024
OBJECTIVE: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy ...
Journal of X-ray science and technology
Jan 1, 2024
BACKGROUND: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies' diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid r...
The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and comp...
Journal of the American Medical Informatics Association : JAMIA
Nov 17, 2023
OBJECTIVE: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%.
Journal of the American Medical Informatics Association : JAMIA
Oct 19, 2023
Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate rep...
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. Howeve...
MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedic...
Adverse drug events (ADEs) are common in clinical practice and can cause significant harm to patients and increase resource use. Natural language processing (NLP) has been applied to automate ADE detection, but NLP systems become less adaptable when ...