Multimodal Generative AI for Missing Physiological Data Imputation in Long-Term Health Monitoring
Keywords:
missing physiological data; multimodal generative artificial intelligence; long-term health monitoring; data imputation; system architecture; data governance; algorithmic fairness; clinical deploymentAbstract
Long-term health monitoring systems increasingly rely on continuous streams of multimodal physiological signals produced by wearable and ambient sensors. These signals include electrocardiography, photoplethysmography, accelerometry, temperature, respiration, and biochemical indicators collected over extended periods in uncontrolled environments. Missing physiological data are pervasive in such systems because of sensor displacement, battery depletion, motion artifact, communication interruptions, synchronization failures, and patient nonadherence. Conventional imputation methods based on linear assumptions often fail to preserve the complex cross-modal dependencies and temporal dynamics that characterize physiological time series. This paper examines the system-level use of multimodal generative artificial intelligence for missing physiological data imputation. It discusses variational autoencoders, generative adversarial networks, attention-based architectures, and related generative frameworks as components within broader monitoring infrastructures. The analysis emphasizes structural trade-offs among reconstruction fidelity, uncertainty calibration, computational burden, privacy preservation, and clinical interpretability. The paper further addresses governance, regulatory compliance, deployment, robustness, fairness, and lifecycle management. Rather than treating imputation as an isolated statistical task, the paper argues that generative imputation must be embedded within a socio-technical ecosystem that includes data quality assurance, equity auditing, clinical validation, and sustainable operation. The discussion concludes that multimodal generative imputation can materially improve the continuity and analytical value of long-term physiological monitoring, but only when algorithmic performance is accompanied by responsible system integration.
References
1. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley.
2. Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 338, b2393.
3. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1-67.
4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
5. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. International Conference on Learning Representations.
6. Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. Proceedings of the 31st International Conference on Machine Learning.
7. Yoon, J., Jordon, J., & van der Schaar, M. (2018). GAIN: Missing data imputation using generative adversarial nets. Proceedings of the 35th International Conference on Machine Learning.
8. Mattei, P.-A., & Frellsen, J. (2019). MIWAE: Deep generative modelling and imputation of incomplete data sets. Proceedings of the 36th International Conference on Machine Learning.
9. Nazabal, A., Olmos, P. M., Ghahramani, Z., & Valera, I. (2020). Handling incomplete heterogeneous data using VAEs. Pattern Recognition, 107, 107501.
10. Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
11. Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112-134.
12. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589-1604.
13. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1, 18.
14. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
15. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
16. Zheng, X., Hu, S., Dwyer, V., Barrett, L., & Derakhshani, M. (2026). Joint attention mechanism learning to facilitate opto-physiological monitoring during physical activity. Biomedical Signal Processing and Control, 113, 108949.
17. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340.
18. Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021). Ethical machine learning in healthcare. Annual Review of Biomedical Data Science, 4, 123-144.
19. 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.
20. 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.
21. 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.
22. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milletari, F., Rubin, D. L., Geras, K. J., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10, 12598.
23. Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020). Challenges to the reproducibility of machine learning in health care. JAMA, 323(4), 305-306.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bioinformatics Insights and Analytics
This work is licensed under a Creative Commons Attribution 4.0 International License.