Streaming EHR Intelligence for Precision Medicine
Keywords:
Electronic Health Record (EHR) Analytics,Precision Medicine,AI-Driven Clinical Data Engineering,Deep Healthcare Architectures,Real-Time Healthcare Analytics,Clinical Decision Support Systems (CDSS),Healthcare Big Data Integration,Predictive Healthcare Modeling,Machine Learning in Healthcare,Intelligent Clinical Data Management.Abstract
Medicine is increasingly moving toward a personalized approach, with the goal of delivering the right treatment to the right patient at the right time. In critical care settings such as the intensive care unit and the emergency department, timely assessment of mortality risk or the need for curative-intent therapy is central to clinical decision-making. This goal is supported by the passive surveillance of patients who are continuously monitored and routinely tested. Translational efforts in this space combine clinical data engineering with machine-learning methods to build real-time systems capable of detecting patient deterioration and informing clinical decisions. The growing availability of data stored in electronic health record systems has fueled strong interest in automated patient management, with the potential to augment clinical judgment or, in some cases, trigger risk-averse intervention automatically.
To realize this potential, decision-support systems must be developed and validated to reliably and rapidly estimate a patient's risk of various outcomes over the course of a hospital admission. Carefully designed stopping criteria determine when such a system should issue a confident prediction versus remain in a "wait and see" state. These criteria evaluate not just predictive accuracy but also the validity of a committed prediction over time. Consistency analysis plays a key role here, ensuring that predictions align with expected clinical behavior — they should not flip or drift unpredictably, and most patients should not remain indefinitely in an active waiting state.
References
1. Li, Y., Rao, S., Ayala Solares, J. R., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020). BEHRT: Transformer for electronic health records. Scientific Reports, 10, 7155.
2. Ma, L., Zhang, C., Wang, Y., Ruan, W., Wang, J., Tang, W., Ma, X., Gao, X., & Gao, J. (2020). ConCare: Personalized clinical feature embedding via capturing the healthcare context. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 833–840.
3. Shah, N., Konchak, C., Chertok, D., Au, L., Kozlov, A., Ravichandran, U., et al. (2020). Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models. PLOS ONE, 15(8), e0238065.
4. Challa, K., Challa, S. R., Pamisetty, A., Kaulwar, P. K., & Krishna Reddy Koppolu, H. (2025). Transforming Payments: The Role of AI and Big Data in Fraud Alerts, Credit Monitoring, and Secure Transactions. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 1406–1414). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484733
5. Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021). Med-BERT: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Medicine, 4, 86.
6. Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J. K., Wanyan, T., Johnson, K. W., Bicak, M., Klang, E., Kwon, Y. J., Costa, A., Zhao, S., Miotto, R., Charney, A. W., Böttinger, E., Fayad, Z. A., Nadkarni, G. N., Wang, F., & Glicksberg, B. S. (2021). Federated learning of electronic health records to improve mortality prediction in hospitalized patients with COVID-19: Machine learning approach. JMIR Medical Informatics, 9(1), e24207.
7. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Govind, D., et al. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.
8. Maguluri, K. K., Chandrasekar, M., Gorshetwar, S. D., Prasanna, S., Anusuya, M., & Vallarasu, K. (2025, February). Applications and advancements of nanoparticle-based drug delivery in lung cancer treatment. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-6). IEEE.
9. Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The Fast Health Interoperability Resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 9(7), e21929.
10. Linder, J. E., Bastarache, L., & Hughey, J. J. (2021). The role of electronic health records in advancing genomic medicine. Annual Review of Genomics and Human Genetics, 22, 219–238.
11. Rajendran, S., Obeid, J. S., Binol, H., D’Agostino, R., Foley, K., Zhang, W., Austin, P., Brakefield, J., Gurcan, M. N., & Topaloglu, U. (2021). Cloud-based federated learning implementation across medical centers. JCO Clinical Cancer Informatics, 5, 1–11.
12. Ande, R., Mehta, D., Pandugula, C., Krishna AzithTejaGanti, V., & Kalisetty, S. (2025). Modeling the Som Kamla Amba sub-watershed using Artificial Neural Networks (ANN) and the SWAT tool. Available at SSRN 5341755.
13. Li, Y., Mamouei, M., Salimi-Khorshidi, G., Rao, S., Hassaine, A., Canoy, D., Lukasiewicz, T., & Rahimi, K. (2023). Hi-BEHRT: Hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records. IEEE Journal of Biomedical and Health Informatics, 27(2), 1106–1117.
14. Sadasivuni, S., Saha, M., Bhatia, N., Banerjee, I., & Sanyal, A. (2022). Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Scientific Reports, 12, 5711.
15. Dang, T. K., Lan, X., Weng, J., & Feng, M. (2022). Federated learning for electronic health records. ACM Transactions on Intelligent Systems and Technology, 13(5), Article 72.
16. Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., Harle, C. A., Lipori, G., Mitchell, D. A., Hogan, W. R., Shenkman, E. A., Bian, J., & Wu, Y. (2022). GatorTron: A large clinical language model to unlock patient information from unstructured electronic health records. npj Digital Medicine, 5, 173.
17. Sivanand, R., Kumar, D. P., Nagabhyru, K. C., Natarajan, E. P., Pamisetty, V., & Kapila, D. (2025, September). IoT and AI for Real-Time Monitoring in Substation Automation. In 2025 International Conference on Computing and Communications (COMPUTINGCON) (pp. 1-5). IEEE.
18. Xu, R., Zhang, Y., Liu, Y., Chen, Y., & others. (2022). Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations. Nature Communications, 13, 675.
19. Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., Lehman, L.-W. H., Celi, L. A., & Mark, R. G. (2023). MIMIC-IV, a freely accessible electronic health record dataset. Scientific Data, 10, 1.
20. Yang, Z., Mitra, A., Liu, W., Berlowitz, D., & Yu, H. (2023). TransformEHR: Transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nature Communications, 14, 7857.
21. Wornow, M., Thapa, R., Steinberg, E., Fries, J., & Shah, N. (2023). EHRSHOT: An EHR benchmark for few-shot evaluation of foundation models. Proceedings of Machine Learning Research.
22. Zhang, X., Zhang, Y., Zhang, H., & others. (2023). Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. Journal of Biomedical Informatics, 146, 104487.
23. Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., Harle, C. A., Lipori, G., Mitchell, D. A., Hogan, W. R., Shenkman, E. A., Bian, J., & Wu, Y. (2022). A large language model for electronic health records. npj Digital Medicine, 5, 145.
24. Kraljevic, Z., Bean, D., Shek, A., Bendayan, R., Hemingway, H., Yeung, J. A., Deng, A., Baston, A., Ross, J., Idowu, E., Teo, J. T., & Dobson, R. J. B. (2024). Foresight—A generative pretrained transformer for modelling of patient timelines using electronic health records: A retrospective modelling study. The Lancet Digital Health, 6(4), e281–e290.
25. Guo, L. L., Fries, J., Steinberg, E., Fleming, S. L., Morse, K., Aftandilian, C., Posada, J., Shah, N., & Sung, L. (2024). A multi-center study on the adaptability of a shared foundation model for electronic health records. npj Digital Medicine, 7, 171.
26. Ande, R., Pandugula, C., Mehta, D., Vankayalapati, R., Birbal, P., Verma, S., ... & Nanavati, N. (2025). Understanding climate change impacts on streamflow by using machine learning: case study of Godavari Basin. Water, 17(8), 1171.
27. Karami, H., Atienza, D., & Ionescu, A. (2024). TEE4EHR: Transformer event encoder for better representation learning in electronic health records. Artificial Intelligence in Medicine, 154, 102903.
28. Allen, B. (2024). The promise of explainable AI in digital health for precision medicine: A systematic review. Journal of Personalized Medicine, 14(3), 277.
29. Robertson, A. J., Mallett, A. J., Stark, Z., & Sullivan, C. (2024). It is in our DNA: Bringing electronic health records and genomic data together for precision medicine. JMIR Bioinformatics and Biotechnology, 5, e55632.
30. Yang, Z., Liu, Y., Mitra, A., Berlowitz, D., & Yu, H. (2024). Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs. npj Digital Medicine, 7.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2026 Dasari Vinay (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain full copyright of their published work. Under this license, others are free to share, copy, distribute, transmit, remix, transform, and build upon the published work for any purpose, including commercial use, provided that appropriate credit is given to the original authors, a link to the license is provided, and any changes made are clearly indicated. No additional restrictions may be applied that limit others from doing anything the license permits. All published articles are freely and permanently accessible online to readers worldwide without any subscription or access fees.
License URL: https://creativecommons.org/licenses/by/4.0/
