Fusing mechanistic and data-driven models for decision making in dynamic environments (real-time information on the patient’s cardiovascular status, expected trajectory and underlying disease processes)

Key Publications:

• Ravid Tannenbaum, N., Gottesman, O., Assadi, A., Mazwi, M., Shalit, U., & Eytan, D. (2023). iCVS—Inferring cardio-vascular hidden states from physiological signals available at the bedside. PLOS Computational Biology, 19(9), e1010835. https://doi.org/10.1371/journal.pcbi.1010835
• Ehrmann, D. E., Joshi, S., Goodfellow, S. D., Mazwi, M. L., & Eytan, D. (2023). Making machine learning matter to clinicians: Model actionability in medical decision-making. npj Digital Medicine, 6(1), 7. https://doi.org/10.1038/s41746-023-00753-7
• Azriel, R., Hahn, C. D., De Cooman, T., Van Huffel, S., Payne, E. T., McBain, K. L., Eytan, D., & Behar, J. A. (2022). Machine learning to support triage of children at risk for epileptic seizures in the pediatric intensive care unit. Physiological Measurement, 43(9). https://doi.org/10.1088/1361-6579/ac8ccd
• Eini-Porat, B., Amir, O., Eytan, D., & Shalit, U. (2022). Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. Journal of Biomedical Informatics, 132, 104107. https://doi.org/10.1016/j.jbi.2022.104107

Conference Proceedings & Presentations:

• Eini-Porat, B., Eytan, D., & Shalit, U. (2024). Aiming for relevance. AMIA Joint Summits on Translational Science Proceedings, 2024, 145–154. https://pmc.ncbi.nlm.nih.gov/articles/PMC11141809/
• Belogolovsky, S., Greenberg, I., Eytan, D., & Mannor, S. (2023). Individualized dosing dynamics via neural eigen decomposition. Advances in Neural Information Processing Systems, 36, 56211–56233. https://doi.org/10.48550/arXiv.2306.14020
• Eytan, D. (2024). The (yet to be fulfilled) potential of rich physiological datasets. Presented at the PEDS Cardio AI Conference, Austin, TX, USA.