Thomas Heldt

Thomas Heldt is an associate professor in MIT’s Department of Electrical Engineering and Computer Science, and a principal investigator with the Research Laboratory of Electronics. He also leads the Integrative Neuromonitoring and Critical Care Informatics Group. Heldt’s research interests focus on signal processing, mathematical modeling, and model identification to support real-time clinical decision making, monitoring of disease progression, and titration of therapy, primarily in neurocritical and neonatal critical care. In particular, he is interested in developing a mechanistic understanding of physiologic systems, and in formulating appropriately chosen computational physiologic models for improved patient care. His research is conducted in close collaboration with colleagues at MIT and clinicians from Boston Children’s Hospital, Beth Israel Deaconess Medical Center, and Boston Medical Center. Heldt earned a degree in physics and medicine from Johannes Gutenberg University, Mainz, an MPhil in physics from Yale University, and a PhD in medical physics from MIT’s Division of Health Sciences and Technology.

Publications

• Filbin, M.R., Thorsen, J.E., Zachary, T.M., Lynch, J.C., Matsushima, M., Belsky, J. B., Heldt, T., Reisner, A.T. (2020). Antibiotic Delays and Feasibility of a 1-Hour-From-Triage Antibiotic Requirement: Analysis of an Emergency Department Sepsis Quality Improvement Database. Annals of Emergency Medicine 75 (1), 93-99.
• Wadehn, F., Weber, T., Mack, D., Heldt, T., Loeliger, H-A. (2020). A framework for model-based separation, detection, and classification of eye movements. IEEE Transactions on Biomedical Engineering 67(2):588-600.
• Lai, H-Y., Saavedra-Peña, G., Sodini, C.G., Sze, V., Heldt, T. (2020). Measuring Saccade Latency using Smartphone Cameras. IEEE Journal of Biomedical and Health Informatics, 24(3):885-897.
• Fanelli, A., Vonberg, F., LaRovere, K., Walsh, B., Smith, E., Robinson, S., Tasker, R.C., Heldt, T. (2019). Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children. Journal of Neurosurgery: Pediatrics 24(5): 509-519.

Media

• March 17, 2021: MIT News, The physicist and the hospital.
• August 23, 2019: MIT News, A much less invasive way to monitor pressure in the brain. 
• November 7, 2018: MIT News, Machine-learning system could aid critical decisions in sepsis care.