Products

Acute Care Innovations

This project sought to develop informatics-based tools and advanced computational algorithms for critical care medicine, with applications to traumatic brain injury, acute respiratory failure, and acute heart failure. We developed solutions for complex tasks such as identifying clinical subgroups, predicting short-term and long-term outcomes for critically ill patients from multimodal physiologic and time-series clinical data, as well as interpreting and explaining model behavior to users. This work was supported in part by the National Science Foundation under grant #1838745.

Time-series Modeling

"Time is the invisible architect of all our stories."

• Ghaderi, H., Foreman, B., Nayebi, A., Tipirneni, S., Reddy, C. K., & Subbian, V. (2023). A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping. Journal of Biomedical Informatics, 143, 104401.
• Pungitore, S., & Subbian, V. (2023). Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research, 7(3), 313-331.
• Ghaderi, H., Foreman, B., Nayebi, A., Tipirneni, S., Reddy, C. K., & Subbian, V. (2023). Identifying TBI physiological states by clustering multivariate clinical time-series data. In AMIA Annual Symposium Proceedings (Vol. 2023, p. 379). American Medical Informatics Association.
• Ghaderi, H., Foreman, B., Reddy, C. K., & Subbian, V. (2024). Discovery of generalizable TBI phenotypes using multivariate time-series clustering. Computers in Biology & Medicine, 180, 108997.
• Nayebi, A., Tipirneni, S., Foreman, B., Ratcliff, J., Reddy, C. K., & Subbian, V. (2022, February). Recurrent neural network based time-series modeling for long-term prognosis following acute traumatic brain injury. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 900).
• Essay, P., Balkan, B., & Subbian, V. (2020). Decompensation in critical care: early prediction of acute heart failure onset. JMIR Medical Informatics, 8(8), e19892.

Explainable AI

• Nayebi, A., Tipirneni, S., Reddy, C. K., Foreman, B., & Subbian, V. (2023). WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. Journal of Biomedical Informatics, 144, 104438.
• Nayebi, A., Tipirneni, S., Foreman, B., Reddy, C. K., & Subbian, V. (2023). An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury. AMIA Annual Symposium Proceedings. 2022, 815–824.

Phenotyping & Respiratory Care

We pioneered a comprehensive effort to advance electronic phenotyping for respiratory care, including first-of-its-kind rule-based phenotyping algorithm to stratify patient records by ventilation strategies. This algorithm has been rigorously validated and applied to both de novo hypoxemic respiratory failure and COVID-19 associated respiratory failure. This work was supported in part by the Emergency Medicine Foundation.

Acute Respiratory Failure

• Essay, P., Mosier, J., & Subbian, V. (2020). Rule-based cohort definitions for acute respiratory failure: electronic phenotyping algorithm. JMIR Medical Informatics, 8(4), e18402.
• Essay, P., Fisher, J. M., Mosier, J. M., & Subbian, V. (2022). Validation of an electronic phenotyping algorithm for patients with acute respiratory failure. Critical Care Explorations, 4(3), e0645.
• Mosier, J. M., Subbian, V., Pungitore, S., Prabhudesai, D., Essay, P., Bedrick, E. J., Stocking, J. C., & Fisher, J. M. (2024). Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure. PloS one, 19(9), e0307849.
• Essay, P., Mosier, J. M., Nayebi, A., Fisher, J. M., & Subbian, V. (2023). Predicting failure of noninvasive respiratory support using deep recurrent learning. Respiratory Care, 68(4), 488-496

COVID-19

• Fisher, J. M., Subbian, V., Essay, P., Pungitore, S., Bedrick, E. J., & Mosier, J. M. (2024). Acute Respiratory Failure From Early Pandemic COVID-19: Noninvasive Respiratory Support vs Mechanical Ventilation. CHEST critical care, 2(1), 100030.
• Essay, P., Mosier, J., & Subbian, V. (2021). Phenotyping COVID-19 patients by ventilation therapy: Data quality challenges and cohort characterization. Studies in Health Technology and Informatics, (pp. 198-202). IOS Press.
• Miller, D. C., Beamer, P., Billheimer, D., Subbian, V., Sorooshian, A., Campbell, B. S., & Mosier, J. M. (2020). Aerosol risk with noninvasive respiratory support in patients with COVID‐19. Journal of the American College of Emergency Physicians Open, 1(4), 521-526.

PASC

Post-acute Sequelae of SARS-COV-2 (PASC) or Long COVID
* Pungitore, S., Olorunnisola, T., Mosier, J., Subbian, V., & N3C Consortium (2024). Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis. AMIA Annual Symposium Proceedings, 2023, 589–598.

Clinical Decision Support Systems

Drug-Drug Interactions

This research developed and validated a range of computable artifacts for drug-drug interaction clinical decision support. Funded by the Agency for Healthcare Research & Quality under grant #R01HS025984 (PI: D. Malone), the project developed algorithms for eight key drug-drug interactions that were frequently overridden and/or considered important by prescribers. Designed to reduce alert fatigue and improve patient safety, these algorithms allow for contextual alerting based on patient-specific data, along with evidence-based explanations.

• Chou, E., Boyce, R. D., Balkan, B., Subbian, V., Romero, A., Hansten, P. D., Horn, J. R., Gephart, S., & Malone, D. C. (2021). Designing and evaluating contextualized drug–drug interaction algorithms. JAMIA Open, 4(1), ooab023.
• Zhang, T., Gephart, S. M., Subbian, V., Boyce, R. D., Villa-Zapata, L., Tan, M. S., Horn, J., Gomez-Lumbreras, A., Romero, A.V. & Malone, D. C. (2023). Barriers to Adoption of Tailored Drug–Drug Interaction Clinical Decision Support. Applied Clinical Informatics, 14(04), 779-788.
• Villa Zapata, L., Subbian, V., Boyce, R. D., Hansten, P. D., Horn, J. R., Gephart, S. M., Romero, A., & Malone, D. C. (2022). Overriding drug-drug interaction alerts in clinical decision support systems: a scoping review. Studies in Health Technology and Informatics (MEDINFO 2021), 380-384.
• Public Website with all products: Drug-Drug Interaction Clinical Decision Support, https://ddi-cds.org/

Telemedicine

• Essay, P., Zhang, T., Mosier, J., & Subbian, V. (2023). Managed Critical Care: Impact of Remote Decision-Making on Patient Outcomes. American Journal of Managed Care, 29(7).
• Zhang, T., Mosier, J., & Subbian, V. (2021). Identifying barriers to and opportunities for telehealth implementation amidst the COVID-19 pandemic by using a human factors approach: a leap into the future of health care delivery?. JMIR human factors, 8(2), e24860.
• Essay, P., Shahin, T. B., Balkan, B., Mosier, J., & Subbian, V. (2019). The connected intensive care unit patient: exploratory analyses and cohort discovery from a critical care telemedicine database. JMIR medical informatics, 7(1), e13006.

Ethics & Informatics

• Subbian, V., Galvin, H. K., Petersen, C., & Solomonides, A. (2021). Ethical, Legal, and Social Issues (ELSI) in Mental Health Informatics. Mental Health Informatics: Enabling a Learning Mental Healthcare System, 479-503.
• Subbian, V., Solomonides, A., Clarkson, M., Rahimzadeh, V. N., Petersen, C., Schreiber, R., DeMuro, P.R., Dua, P., Goodman, K.W., Kaplan, B., Koppel, R., Lehmann, C. U., Pan, E., & Senathirajah, Y. (2021). Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy. Journal of the American Medical Informatics Association, 28(1), 184-189.
• Petersen, C., Berner, E. S., Cardillo, A., Fultz Hollis, K., Goodman, K. W., Koppel, R., Korngiebel, D.M., Lehmann, C.U., Solomonides, A.E., & Subbian, V. (2023). AMIA’s code of professional and ethical conduct 2022. Journal of the American Medical Informatics Association, 30(1), 3-7.
• Petersen, C., & Subbian, V. (2020). Special section on ethics in health informatics. Yearbook of Medical Informatics, 29(01), 077-080.