Advances in Engineering Innovation
Vol. 8, 28 June 2024
Open
Access | Article
Application of big data technology in the medical field
* Author to whom correspondence should be addressed.
Abstract
This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and database operations. In the future, there is a desire to further improve technical capabilities, explore more cutting-edge technologies in the TCM field, and promote project progress through collaboration, contributing to the modernization of TCM and intelligent healthcare services.
Keywords
TCM knowledge graph, entity recognition technology, Neo4j database, application of knowledge graph
References
1. Liu, J., Li, Y., Duan, H., et al. (2016). A review of knowledge graph construction techniques. Journal of Computer Research and Development, 53(3), 582-600.
2. Xu, Z., Sheng, Y., He, L., et al. (2016). A review of knowledge graph technology. Journal of University of Electronic Science and Technology of China, 45(4), 589-606.
3. Jia, L., Liu, J., Yu, T., et al. (2015). Construction of traditional Chinese medicine knowledge graph. Journal of Medical Informatics, 36(8), 51-53+59.
4. Ruan, T., Sun, C., Wang, H., et al. (2016). Construction and application of traditional Chinese medicine knowledge graph. Journal of Medical Informatics, 37(4), 8-13.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- ISBN (Print)
- ISBN (Online)
- Published Date
- 28 June 2024
- Series
- Advances in Engineering Innovation
- ISSN (Print)
- 2977-3903
- ISSN (Online)
- 2977-3911
- DOI
- 10.54254/2977-3903/8/2024083
- Copyright
- 28 June 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited