Standards Mapping in Clinical Trials Using LLMs with Knowledge Graph Embeddings
Keywords:
clinical trials, knowledge graphs, large language models, TransE embeddings, ComplEx embeddings, ICH-GCP, regulatory complianceAbstract
Large language models (LLMs) and knowledge graph embeddings automate clinical trial documentation standards mapping for ICH-GCP and FDA 21 CFR Part 11 compliance in this research. TransE and ComplEx embedding provide a semantically improved knowledge network of clinical trial entities, processes, and regulatory duties. The recommended method eliminates human annotation and mapping errors that inhibit multi-site, global clinical trial submissions by aligning trial materials with regulatory standards. In empirical testing, LLM-guided reasoning beats graph-structured representations in discovering non-compliance and suggesting corrective mappings. The strategy speeds regulatory review by improving traceability, interpretability, and homogeneity among clinical datasets. We found that deep contextual language understanding and structured knowledge embeddings improve clinical research compliance, operational risk, and documentation.
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S. H. Goodman, L. Chen, and R. M. Taylor, “Ensuring compliance in global clinical trials: Challenges and solutions under ICH-GCP and FDA 21 CFR Part 11,” Journal of Clinical Research Best Practices, vol. 17, no. 3, pp. 45–62, 2020.
M. N. Patel and G. K. Wilson, “AI-based automation of regulatory documentation in clinical research: Emerging paradigms,” Drug Information Journal, vol. 54, no. 6, pp. 705–718, 2021.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Advances in Neural Information Processing Systems (NeurIPS), pp. 3111–3119, 2013.
A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” Advances in Neural Information Processing Systems (NeurIPS), vol. 26, pp. 2787–2795, 2013.
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” Proceedings of the 33rd International Conference on Machine Learning (ICML), pp. 2071–2080, 2016.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, 2014.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 4171–4186, 2019.
T. Brown et al., “Language models are few-shot learners,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020.
Y. Zhang, Z. Chen, and W. Zhao, “Knowledge graph construction and completion in clinical research: Methods and applications,” Journal of Biomedical Informatics, vol. 117, no. 1, pp. 103–123, 2021.
P. Ristoski and H. Paulheim, “Semantic web in data mining and knowledge discovery: A comprehensive survey,” Journal of Web Semantics, vol. 36, pp. 1–22, 2016.
A. Zaveri, D. Kontokostas, M. A. Sherif, R. Cyganiak, and J. Lehmann, “Quality assessment for linked data: A survey,” Semantic Web Journal, vol. 7, no. 1, pp. 63–93, 2016.
U.S. Food and Drug Administration (FDA), “Title 21 Code of Federal Regulations Part 11—Electronic Records; Electronic Signatures,” Federal Register, vol. 62, no. 54, pp. 13430–13466, 1997.
International Council for Harmonisation (ICH), “E6(R2): Good Clinical Practice: Consolidated Guideline,” ICH Harmonised Tripartite Guideline, 2016.
H. Chen, C. Xie, and Z. Li, “Ontology-based text mining for regulatory compliance in clinical trial documentation,” Artificial Intelligence in Medicine, vol. 112, pp. 102–114, 2021.
C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard, and D. McClosky, “The Stanford CoreNLP natural language processing toolkit,” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 55–60, 2014.
J. Peters and D. Blanchard, “Automating regulatory intelligence with AI: A knowledge graph-based approach to compliance mapping,” Regulatory Toxicology and Pharmacology, vol. 127, pp. 105–121, 2023.
M. Krallinger, O. Rabal, A. Akhondi, M. Pérez, and A. Valencia, “Overview of the BioCreative VI chemical–protein interaction track,” Database: The Journal of Biological Databases and Curation, vol. 2017, pp. 1–11, 2017.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and P. S. Yu, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494–514, 2022.
N. Liu, Q. Lin, and D. Gao, “Application of natural language processing for regulatory compliance monitoring in clinical research,” Frontiers in Pharmacology, vol. 12, pp. 774–785, 2021.
Y. Yuan, X. Song, and L. Xu, “AI-driven compliance validation for multi-site clinical trials: Integrating contextual embeddings and regulatory ontologies,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1592–1605, 2023.
