[HTML][HTML] Knowledge graph applications in medical imaging analysis: a scoping review
Background. There is an increasing trend to represent domain knowledge in structured
graphs, which provide efficient knowledge representations for many downstream tasks …
graphs, which provide efficient knowledge representations for many downstream tasks …
[HTML][HTML] Early prediction of ICU readmissions using classification algorithms
M Loreto, T Lisboa, VP Moreira - Computers in biology and medicine, 2020 - Elsevier
Context: Determining which patients are ready for discharge from an Intensive Care Unit
(ICU) presents a huge challenge, as ICU readmissions are associated with several negative …
(ICU) presents a huge challenge, as ICU readmissions are associated with several negative …
Self-explaining neural network with concept-based explanations for ICU mortality prediction
Complex deep learning models show high prediction tasks in various clinical prediction
tasks but their inherent complexity makes it more challenging to explain model predictions …
tasks but their inherent complexity makes it more challenging to explain model predictions …
An ensemble learning approach to perform link prediction on large scale biomedical knowledge graphs for drug repurposing and discovery
V Prabhakar, C Vu, J Crawford, J Waite, K Liu - bioRxiv, 2023 - biorxiv.org
Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and
relations (edges) in large scale knowledge graph datasets has been a challenging problem …
relations (edges) in large scale knowledge graph datasets has been a challenging problem …
Integrating graph contextualized knowledge into pre-trained language models
Complex node interactions are common in knowledge graphs, and these interactions also
contain rich knowledge information. However, traditional methods usually treat a triple as a …
contain rich knowledge information. However, traditional methods usually treat a triple as a …
Knowledge graph solutions in healthcare for improved clinical outcomes
J Aasman, P Mirhaji - CEUR Workshop Proceedings, 2018 - einstein.elsevierpure.com
Abstract Deploying patient Knowledge Graphs based on Semantic Technologies offers
improved patient care and revolutionizes care models and medical research. Knowledge …
improved patient care and revolutionizes care models and medical research. Knowledge …
A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records
Due to high costs, resources and managemant associated with readmission into Intensive
Care Units (ICU), it has been a center of clinical research. Previous research successfully …
Care Units (ICU), it has been a center of clinical research. Previous research successfully …
Hierarchical attention propagation for healthcare representation learning
Medical ontologies are widely used to represent and organize medical terminologies.
Examples include ICD-9, ICD-10, UMLS etc. The ontologies are often constructed in …
Examples include ICD-9, ICD-10, UMLS etc. The ontologies are often constructed in …
Semantic health knowledge graph: semantic integration of heterogeneous medical knowledge and services
With the explosion of healthcare information, there has been a tremendous amount of
heterogeneous textual medical knowledge (TMK), which plays an essential role in …
heterogeneous textual medical knowledge (TMK), which plays an essential role in …
Predicting unplanned readmissions in the intensive care unit: a multimodality evaluation
A hospital readmission is when a patient who was discharged from the hospital is admitted
again for the same or related care within a certain period. Hospital readmissions are a …
again for the same or related care within a certain period. Hospital readmissions are a …