Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study

Murali, Lino; Gopakumar, G.; Viswanathan, Daleesha M.; Nedungadi, Prema

doi:10.1016/j.jbi.2023.104403

Cited by 27 publications

(7 citation statements)

References 81 publications

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“…The applications supported by the knowledge graph are increasingly diverse and have been successfully applied to tasks such as intelligent question answering [1][2][3], personalized recommendations [4][5][6], and interpretable tools [7][8][9]. With the increase in the application of the knowledge graph, people have built large-scale non-domain knowledge graphs, such as DBpedia [10] and Freebase [11], and also built various knowledge graphs around domain application [12][13][14]. The contents of these knowledge graphs are complementary or duplicate.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

Zhang,

Chen,

Wang

2024

Mathematics

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Entity alignment is an important task in knowledge fusion, which aims to link entities that have the same real-world identity in two knowledge graphs. However, in the process of constructing a knowledge graph, some noise may inevitably be introduced, which must affect the results of the entity alignment tasks. The triple confidence calculation can quantify the correctness of the triples to reduce the impact of the noise on entity alignment. Therefore, we designed a method to calculate the confidence of the triples and applied it to the knowledge representation learning phase of entity alignment. The method calculates the triple confidence based on the pairing rates of the three angles between the entities and relations. Specifically, the method uses the pairing rates of the three angles as features, which are then fed into a feedforward neural network for training to obtain the triple confidence. Moreover, we introduced the triple confidence into the knowledge representation learning methods to improve their performance in entity alignment. For the graph neural network-based method GCN, we considered entity confidence when calculating the adjacency matrix, and for the translation-based method TransE, we proposed a strategy to dynamically adjust the margin value in the loss function based on confidence. These two methods were then applied to the entity alignment, and the experimental results demonstrate that compared with the knowledge representation learning methods without integrating confidence, the confidence-based knowledge representation learning methods achieved excellent performance in the entity alignment task.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

Zhang,

Chen,

Wang

2024

Mathematics

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“…8 The inefficient recycling, landfill, or incineration of waste EEE is a serious hazard to the environment and human health because of the existence of many toxic and harmful (T&H) materials. 9 To address this problem, many countries are promoting EEE green design by issuing official documents to reduce the possible environmental impacts throughout the life cycle of EEE at the design stage. 10 For instance, the directive on energy-related products (ErPs) mandates adherence to green design principles for the products sold in Europe.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Knowledge graph (KG) is a structured knowledge representation method that can integrate information from different data sources and capture complex relationships between elements. 27 It has been widely used in financial fraud detection, 28 medical diagnosis, 9 and enterprise knowledge management, 29 but rarely applied to EEE green design evaluation. This study applied KG technology to develop a green design evaluation method of EEE and provided a holistic analysis of the importance differences of various green design indexes (i.e., a set of metrics used to assess the sustainability of green design products) and the green design levels (i.e., product performance levels in the aspects considered by green design) of different products.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Accordingly, the amount of waste EEE is projected to increase by 40% from 2020 to 2030 . The inefficient recycling, landfill, or incineration of waste EEE is a serious hazard to the environment and human health because of the existence of many toxic and harmful (T&H) materials . To address this problem, many countries are promoting EEE green design by issuing official documents to reduce the possible environmental impacts throughout the life cycle of EEE at the design stage .…”

Section: Introductionmentioning

confidence: 99%

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Green Design Evaluation of Electrical and Electronic Equipment Based on Knowledge Graph

Dang,

Wang,

et al. 2023

ACS Sustainable Chem. Eng.

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“…At the same time, Baidu also followed Google's technology and launched the Chinese Knowledge Graph [8,9]. Subsequently, a knowledge graph began to rapidly extend from search engines to finance [10], agriculture [11], medical [12,13] and other industries, and became an indispensable technical means for integrating and analyzing data in the era of big data.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

Li,

Guo,

et al. 2023

Applied Sciences

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Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.

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Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study

Cited by 27 publications

References 81 publications

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

Green Design Evaluation of Electrical and Electronic Equipment Based on Knowledge Graph

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

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