References

Banerjee, I., Ling, Y., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., ... & Lungren, M. P. (2019). Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artificial Intelligence in Medicine, 97, 79-88.

Johnson, S. J., Murty, M. R., & Navakanth, I. (2024). A detailed review on word embedding techniques with emphasis on word2vec. Multimedia Tools and Applications, 83(13), 37979-38007.

Langlotz, C. P. (2006). RadLex: A new method for indexing online educational materials. Radiographics, 26(6), 1595-1597.

Li, Z., Roberts, K., Jiang, X., & Long, Q. (2019). Distributed learning from multiple EHR databases: contextual embedding models for medical events. Journal of Biomedical Informatics, 92, 103138.

Liu, Y., Ge, T., Mathews, K. S., Ji, H., & McGuinness, D. L. (2018). Exploiting task-oriented resources to learn word embeddings for clinical abbreviation expansion. arXiv preprint arXiv:1804.04225.

Ma, L., Zhang, C., Wang, Y., Ruan, W., Wang, J., Tang, W., ... & Gao, J. (2020, April). Concare: Personalized clinical feature embedding via capturing the healthcare context. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 833-840).

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6(1), 1-10.

Neelima, S., Govindaraj, M., Subramani, K., ALkhayyat, A., & Mohan, C. (2024). Factors influencing data utilization and performance of health management information systems: A case study. Indian Journal of Information Sources and Services, 14(2), 146-152. https://doi.org/10.51983/ijiss-2024.14.2.21

Nobel, J. M., Puts, S., Bakers, F. C., Robben, S. G., & Dekker, A. L. (2020). Natural language processing in Dutch free text radiology reports: challenges in a small language area staging pulmonary oncology. Journal of Digital Imaging, 33, 1002-1008.

Pudasaini, S., Shakya, S., Lamichhane, S., Adhikari, S., Tamang, A., & Adhikari, S. (2022). Application of NLP for information extraction from unstructured documents. In Expert Clouds and Applications: Proceedings of ICOECA 2021 (pp. 695-704). Springer Singapore.

Radiopaedia.org, the wiki-based collaborative Radiology resource. Radiopaedia. https://radiopaedia.org/?lang=us. Accessed June 1, 2020.

Richardson, L. (2007). Beautiful soup documentation.

Shetty, S., & Mahale, A. (2023). Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports. Multimedia Tools and Applications, 82(28), 44431-44478.

Sindhusaranya, B., Yamini, R., Manimekalai Dr, M. A. P., & Geetha Dr, K. (2023). Federated learning and blockchain-enabled privacy-preserving healthcare 5.0 system: A comprehensive approach to fraud prevention and security in IoMT. Journal of Internet Services and Information Security, 13(4), 199-209.

Sorin, V., Barash, Y., Konen, E., & Klang, E. (2020). Deep learning for natural language processing in radiology—fundamentals and a systematic review. Journal of the American College of Radiology, 17(5), 639-648.

Yang, X., Lyu, T., Li, Q., Lee, C. Y., Bian, J., Hogan, W. R., & Wu, Y. (2019). A study of deep learning methods for de-identification of clinical notes in cross-institute settings. BMC Medical Informatics and Decision Making, 19, 1-9.

Yuan, J., Zhu, H., & Tahmasebi, A. (2019). Classification of pulmonary nodular findings based on characterization of change using radiology reports. AMIA Summits on Translational Science Proceedings, 2019, 285.