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All issues Volume 619 (2025) E3S Web Conf., 619 (2025) 03016 References
Open Access
Issue
E3S Web Conf.
Volume 619, 2025
3rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)
Article Number 03016
Number of page(s) 9
Section Smart Electronics for Sustainable Solutions
DOI https://doi.org/10.1051/e3sconf/202561903016
Published online 12 March 2025
  1. G. Wang, X. Lou, F. Guo, D. Kwok and C. Cao, “EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records,” in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1668-1679, March 2024. [CrossRef] [PubMed] [Google Scholar]
  2. Hossain, Elias, Rajib Rana, Niall Higgins, Jeffrey Soar, Prabal Datta Barua, Anthony R. Pisani, and Kathryn Turner. “Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review.” Computers in biology and medicine 155 (2023): 106649. [CrossRef] [PubMed] [Google Scholar]
  3. Z. Zeng, Y. Deng, X. Li, T. Naumann and Y. Luo, “Natural Language Processing for EHR-Based Computational Phenotyping,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 1, pp. 139-153, 1 Jan.-Feb. 2019. [CrossRef] [PubMed] [Google Scholar]
  4. Li, Irene, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand et al. “Neural natural language processing for unstructured data in electronic health records: a review.” Computer Science Review 46 (2022): 100511. [CrossRef] [Google Scholar]
  5. Koleck, Theresa A., Caitlin Dreisbach, Philip E. Bourne, and Suzanne Bakken. “Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.” Journal of the American Medical Informatics Association 26, no. 4 (2019): 364-379. [CrossRef] [PubMed] [Google Scholar]
  6. Zhang, Rui, Sisi Ma, Liesa Shanahan, Jessica Munroe, Sarah Horn, and Stuart Speedie. “Discovering and identifying New York heart association classification from electronic health records.” BMC medical informatics and decision making 18 (2018): 5-13. [CrossRef] [PubMed] [Google Scholar]
  7. Kaswan, Kuldeep Singh, Loveleen Gaur, Jagjit Singh Dhatterwal, and Rajesh Kumar. “AI-based natural language processing for the generation of meaningful information electronic health record (EHR) data.” In Advanced AI techniques and applications in bioinformatics, pp. 41-86. CRC Press, 2021. [Google Scholar]
  8. Chen, Jingfeng, Wei Wei, Chonghui Guo, Lin Tang, and Leilei Sun. “Textual analysis and visualization of research trends in data mining for electronic health records.” Health Policy and Technology 6, no. 4 (2017): 389-400. [CrossRef] [Google Scholar]
  9. Hariharan, Shanmugasundaram, D. Anandan, Murugaperumal Krishnamoorthy, Vinay Kukreja, Nitin Goyal, and Shih-Yu Chen. “Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models.” CMES- Computer Modeling in Engineering & Sciences 142, no. 1 (2025). [Google Scholar]
  10. Decker, Barbara M., Alexandra Turco, Jian Xu, Samuel W. Terman, Nikitha Kosaraju, Alisha Jamil, Kathryn A. Davis et al. “Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.” Seizure: European Journal of Epilepsy 101 (2022): 48-51. [CrossRef] [Google Scholar]
  11. Chase, Herbert S., Lindsey R. Mitrani, Gabriel G. Lu, and Dominick J. Fulgieri. “Early recognition of multiple sclerosis using natural language processing of the electronic health record.” BMC medical informatics and decision making 17 (2017): 1-8. [CrossRef] [PubMed] [Google Scholar]
  12. Houssein, Essam H., Rehab E. Mohamed, and Abdelmgeid A. Ali. “Machine learning techniques for biomedical natural language processing: a comprehensive review.” IEEE access 9 (2021): 140628-140653. [CrossRef] [Google Scholar]

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