Personalized Healthcare Recommendation Systems Based on Patient Narratives and Large Language Model-Based Self-Representation Learning
Keywords:
personalized healthcare, recommendation systems, patient narratives, large language models, self-representation learning, fairness, system architecture, governanceAbstract
The increasing availability of unstructured patient-generated health narratives, ranging from symptom diaries to psychosocial reflections, presents a transformative opportunity for precision medicine. This paper investigates the design, infrastructure, and governance of personalized healthcare recommendation systems that integrate patient narratives with large language model (LLM)-based self-representation learning. Rather than focusing narrowly on model accuracy, we offer a system-level analysis of the architectural trade-offs, deployment challenges, and regulatory implications inherent in such platforms. We conceptualize self-representation as a dynamic, linguistically encoded construct that LLMs can extract and refine through iterative narrative processing, enabling recommendations that align not only with clinical histories but also with an individual’s evolving self-concept, values, and experiential context. The paper examines the complete pipeline from narrative ingestion and semantic segmentation to the construction of a personal knowledge graph fused with a continuously updated latent self-representation. Key system dimensions are dissected: the tension between centralized and federated architectures for data governance, the robustness of self-representation against distributional shifts and adversarial noise, fairness considerations when modeling subjective identity constructs, and the sustainability of deploying compute-intensive LLMs in health systems. We further address policy implications surrounding data ownership, the right to explanation in algorithmically derived self-models, and the need for auditability. By integrating perspectives from human-computer interaction, clinical informatics, and responsible AI, this work provides a comprehensive framework for building narrative-driven recommendation systems that are clinically safe, ethically grounded, and structurally resilient. The analysis highlights how the fusion of patient narratives and self-representation learning can shift recommendation paradigms from population-based protocols toward truly person-centered care, while also outlining the sociotechnical guardrails necessary to realize that vision.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998–6008).
2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shinn, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (pp. 1877–1901).
3. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Babiker, A., Scharli, N., Chowdhery, A., Mansfield, P., Denner, D., ... Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180.
4. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Cantón, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., ... Scialom, T. (2023). LLaMA 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
5. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
6. Charon, R. (2001). Narrative medicine: A model for empathy, reflection, profession, and trust. JAMA, 286(15), 1897–1902.
7. Insel, T. R. (2017). Digital phenotyping: Technology for a new science of behavior. JAMA, 318(13), 1215–1216.
8. McAdams, D. P. (2001). The psychology of life stories. Review of General Psychology, 5(2), 100–122.
9. Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12(1), 38–48.
10. Leventhal, H., Phillips, L. A., & Burns, E. (2016). The Common-Sense Model of Self-Regulation (CSM): a dynamic framework for understanding illness self-management. Journal of Behavioral Medicine, 39(6), 935–946.
11. Pennebaker, J. W., & Chung, C. K. (2011). Expressive writing: Connections to physical and mental health. In H. S. Friedman (Ed.), The Oxford Handbook of Health Psychology (pp. 417–437). Oxford University Press.
12. Hohman, F., Wongsuphasawat, K., Kery, M. B., & Patel, K. (2021). Understanding and visualizing data iteration in machine learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM.
13. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119.
14. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
15. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308–318). ACM.
16. Solanki, D., Hsu, H. M., Zhao, O., Zhang, R., Bi, W., & Kannan, R. (2020, July). The way we think about ourselves. In International Conference on Human-Computer Interaction (pp. 276-285). Cham: Springer International Publishing.
17. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
18. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
19. European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation). Official Journal of the European Union, L119, 1–88.
20. World Health Organization. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.
21. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
22. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics.
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