Knowledge Graph-Based Clinical Decision Support System Reasoning: A Survey

Xiang, Xiayu; Wang, Zhongru; Jia, Yan; Fang, Binxing

doi:10.1109/dsc.2019.00063

Cited by 13 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge graphs (KGs) store a large amount of structured knowledge, which has been successfully applied in fields such as knowledge reasoning [1], question answering [2] and recommendation [3]. At present, there are some typical large-scale knowledge graphs, such as YAGO [4], DBpedia [5] and Freebase [6].…”

Section: Introductionmentioning

confidence: 99%

Multimodal knowledge graph representation learning with entity description

Sun¹,

Wang²,

Huang³

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

View full text Add to dashboard Cite

The representation learning of knowledge graph aims to represent the semantic information of entities and relations as dense low-dimensional real-valued vectors and map them to the same low-dimensional space. Existing methods often focus on the single-modal information of the text and ignore the information of the image modality, resulting in the ineffective use of entity feature information in the image. And there is entity-related descriptive information in most knowledge graphs, which are not well used in current multimodal knowledge representation learning methods. In this regard, a multimodal knowledge representation learning method combining description information is proposed. This method combines multimodal (image, text) data to construct a knowledge representation learning model and combines its corresponding brief description information to improve the representation effect of multimodal data. Experimental results show that the method performs well on triple classification and link prediction tasks on the constructed WI-D dataset.

show abstract

Section: Introductionmentioning

confidence: 99%

Multimodal knowledge graph representation learning with entity description

Sun¹,

Wang²,

Huang³

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

View full text Add to dashboard Cite

show abstract

“…The research works of the World Health Organization (WHO) [90] concludes that stress is the world mental health disease of the 21st century and may be the trigger for depression and even suicide if it is not treated correctly. WHO estimates that, in the world, suicide is the second cause of death in the group of 15 to 29 years of age and that more than 800,000 people die due to suicide every year.…”

Section: Introductionmentioning

confidence: 99%

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Galindo

Moreno²

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Contribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.

show abstract

“…In recent years, knowledge graphs (KGs) have been established for medical assistance as the underlying core component of clinical decision support systems (CDSSs) [23,17] and self-diagnostic symptom checkers [15]. Those KGs are often extracted from sources such as Electronic Medical Records (EMR) and store clinical information into a set of triples for representing medical entities as nodes and relations as the edges between them.…”

Section: Introductionmentioning

confidence: 99%

Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records

Guluzade¹,

Kacupaj²,

Maleshkova³

2021

Preprint

View full text Add to dashboard Cite

Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.

show abstract

Knowledge Graph-Based Clinical Decision Support System Reasoning: A Survey

Cited by 13 publications

References 43 publications

Multimodal knowledge graph representation learning with entity description

Multimodal knowledge graph representation learning with entity description

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records

Contact Info

Product

Resources

About