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, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge gr...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Healthcare |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9032/11/12/1762 |
| _version_ | 1797594578552881152 |
|---|---|
| author | Bader Aldughayfiq Farzeen Ashfaq N. Z. Jhanjhi Mamoona Humayun |
| author_facet | Bader Aldughayfiq Farzeen Ashfaq N. Z. Jhanjhi Mamoona Humayun |
| author_sort | Bader Aldughayfiq |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:24:20Z |
| format | Article |
| id | doaj.art-44459fc7fd524ac7a8025d29b0e8e2af |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T02:24:20Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-44459fc7fd524ac7a8025d29b0e8e2af2023-11-18T10:38:46ZengMDPI AGHealthcare2227-90322023-06-011112176210.3390/healthcare11121762Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDBBader Aldughayfiq0Farzeen Ashfaq1N. Z. Jhanjhi2Mamoona Humayun3Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaSchool of Computer Science—SCS, Taylor’s University, Subang Jaya 47500, MalaysiaSchool of Computer Science—SCS, Taylor’s University, Subang Jaya 47500, MalaysiaDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaElectronic 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.https://www.mdpi.com/2227-9032/11/12/1762electronic health recordsknowledge graphssemantic relationshipsdata analysisMIMIC IIIGraphDB |
| spellingShingle | Bader Aldughayfiq Farzeen Ashfaq N. Z. Jhanjhi Mamoona Humayun Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB Healthcare electronic health records knowledge graphs semantic relationships data analysis MIMIC III GraphDB |
| title | Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB |
| title_full | Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB |
| title_fullStr | Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB |
| title_full_unstemmed | Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB |
| title_short | Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB |
| title_sort | capturing semantic relationships in electronic health records using knowledge graphs an implementation using mimic iii dataset and graphdb |
| topic | electronic health records knowledge graphs semantic relationships data analysis MIMIC III GraphDB |
| url | https://www.mdpi.com/2227-9032/11/12/1762 |
| work_keys_str_mv | AT baderaldughayfiq capturingsemanticrelationshipsinelectronichealthrecordsusingknowledgegraphsanimplementationusingmimiciiidatasetandgraphdb AT farzeenashfaq capturingsemanticrelationshipsinelectronichealthrecordsusingknowledgegraphsanimplementationusingmimiciiidatasetandgraphdb AT nzjhanjhi capturingsemanticrelationshipsinelectronichealthrecordsusingknowledgegraphsanimplementationusingmimiciiidatasetandgraphdb AT mamoonahumayun capturingsemanticrelationshipsinelectronichealthrecordsusingknowledgegraphsanimplementationusingmimiciiidatasetandgraphdb |