Knowledge graph embedding: A survey from the perspective of representation spaces
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
Knowledge graphs for the life sciences: Recent developments, challenges and opportunities
The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
Capturing semantic relationships in electronic health records using knowledge graphs: An implementation using mimic iii dataset and graphdb
Electronic health records (EHRs) are an increasingly important source of information for
healthcare professionals and researchers. However, EHRs are often fragmented …
healthcare professionals and researchers. However, EHRs are often fragmented …
[HTML][HTML] Electronic Health Record–Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data …
Y Shang, Y Tian, K Lyu, T Zhou, P Zhang… - Journal of Medical …, 2024 - jmir.org
Background The medical knowledge graph provides explainable decision support, helping
clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical …
clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical …
The case for expressing nursing theories using ontologies
EE Umberfield, PA Ball Dunlap… - Journal of the American …, 2023 - academic.oup.com
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 …
of domains, specifically the underlying notion of 'things'(ie, concepts, constructs, or named …
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of …
Automated knowledge curation for biomedical ontologies is key to ensure that they remain
comprehensive, high-quality and up-to-date. In the era of foundational language models …
comprehensive, high-quality and up-to-date. In the era of foundational language models …
A Transformer-Based Model for Zero-Shot Health Trajectory Prediction
Integrating modern machine learning and clinical decision-making has great promise for
mitigating healthcares increasing cost and complexity. We introduce the Enhanced …
mitigating healthcares increasing cost and complexity. We introduce the Enhanced …
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact
representations of entities and relations within a knowledge graph, facilitating efficient …
representations of entities and relations within a knowledge graph, facilitating efficient …
Role of Charlson comorbidity index in predicting the ICU admission in patients with thoracic aortic aneurysm undergoing surgery
Y Zhan, F Li, L Wu, J Li, C Zhu, M Han… - Journal of Orthopaedic …, 2023 - Springer
Objectives This study aimed to explore the value of the Charlson comorbidity index (CCI) in
predicting ICU admission in patients with aortic aneurysm (AA). Methods The clinical data of …
predicting ICU admission in patients with aortic aneurysm (AA). Methods The clinical data of …
CARE-30: A Causally Driven Multi-Modal Model for Enhanced 30-Day ICU Readmission Predictions
L Wang, L Zhao, Z Luo, X Wang… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Accurate prediction of unplanned readmissions allows healthcare systems to adopt
preventive measures, reducing these occurrences. Creating a model that accurately predicts …
preventive measures, reducing these occurrences. Creating a model that accurately predicts …