Medical Knowledge Graph Construction Based on Traceable Conversion

Hou, Wei; Zheng, Wenkui; Sheng, Ming; Ren, Peng; Zuo, Baifu; Hu, Zhentao; Liu, Xianxing; Duan, Yang

doi:10.1007/978-3-031-20627-6_23

Cited by 1 publication

(1 citation statement)

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“…As an illustration, ref. [ 38 ] created a knowledge graph to illustrate the connections between therapeutic ideas and patient outcomes in the MIMIC III dataset. Comparing their knowledge graph to conventional machine learning models, they discovered that their knowledge graph significantly increased the accuracy of forecasting patient outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

et al. 2023

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Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

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Section: Literature Reviewmentioning

confidence: 99%