Research Grants

Research Grants

Collaborative Research: sch: a cASUAL AI DIGITAL TWIN FRAMEWORK TO TRANSFORM INTENSIVE CARE DELIVERY

Funding agency: NSF IIS-2123848 UF (lead institution) PI: Xiang Zhong Mayo Clinic PI: Philips Schulte

Project Description: This Smart and Connected Health (SCH) award will contribute to the advancement of the national health and welfare by developing a causal AI digital twin framework to support critical care delivery and facilitate the realization of “Healthcare 4.0.” Critical illness from sepsis and pneumonia is the leading cause of in-hospital mortality and a global health priority. While early diagnosis and error-free treatment consistently achieve good outcomes, the progression of critical illness to multiple organ failure often translates to either death or loss of independence. The COVID-19 pandemics have exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. The National Academy of Medicine has called for a novel systems science approach to clinical medicine and new methods and strategies to facilitate timely and accurate interventions are needed. This project will provide valuable solutions to critical care delivery by developing a virtual counterpart to the intensive care unit (ICU) bolstered with decision support to inform patient health and care delivery at multiple levels.

Research Highlights:

Please check out a demonstration of ICU simulation in AnyLogic Could [Link]

Publications:

  • Trevena W, Zhong X, Lal A, Rovati L, Cubro E, Dong Y, Schulte P, Gajic O. Model-driven engineering for digital twins: a graph model-based patient simulation application. Frontiers in Physiology. 2024 Aug 12;15:1424931. [Link]
  • Rovati L, Gary PJ, Cubro E, Dong Y, Kilickaya O, Schulte PJ, Zhong X, Wörster M, Kelm DJ, Gajic O, Niven AS. Development and usability testing of a patient digital twin for critical care education: a mixed methods study. Frontiers in Medicine. 2024 Jan 11;10:1336897. [Link]
  • Trevena W, Lal A, Zec S, Cubro E, Zhong X, Dong Y, Gajic O. Modeling of critically ill patient pathways to support intensive care delivery. IEEE Robotics and Automation Letters. 2022 Jun 15;7(3):7287-94. [Link]
  • Zhong X, Abrol S, Hou Y, Dong Y, Lal A, Gajic O, A hybrid simulation approach for modeling critical care delivery in ICU, WSC’24: Proceedings of the Winter Simulation Conference, 2024. [AnyLogic Cloud Link]

EAGER: Data-Driven Susceptible-Exposed-Infected-Recovered-Infected (SEIRI) Modeling and Hos-pital Planning and Operations for COVID-19 Pandemic

Funding agency: NSF CMMI-2027677 PI: Yongpei Guan, co-PI: Xiang Zhong

Project Description: The goal of this project is to develop a data-driven decision-support system, which will allow healthcare workers to determine how to optimally use limited resources to mitigate risk and minimize the consequences throughout the entirety of the disease progression, from early screening to diagnosis and treatment. The model will provide an advanced quantitative analysis of the SEIRI model, which is adaptive to control numbers and can be utilized for regional forecast targeting a hospitals catchment area. This systematic approach will better assist hospitals with planning and operations such as ensuring reliable PPE and staff planning, all while remaining cost-effective.

Research Highlights:

Publications:

  • Yu T, Guan Y, Zhong X. Visiting nurses assignment and routing for decentralized telehealth service networks. Annals of Operations Research. 2024 Mar 5:1-31. [Link]
  • Wang X, Guan Y, Zhong X. Service system design of video conferencing visits with nurse assistance. IISE transactions. 2022 Aug 3;54(8):741-56. [Link]
  • Zhang T, Lu Y, Guan Y, Zhong X, Hogan T. Data-driven modeling and analysis for COVID-19 pandemic hospital beds planning. IEEE Transactions on Automation Science and Engineering. 2022 Nov 29;20(3):1551-64. [Link]
  • Lu Y, Guan Y, Zhong X, Fishe JN, Hogan T. Hospital beds planning and admission control policies for COVID-19 pandemic: A hybrid computer simulation approach. In2021 IEEE 17th International Conference on Automation SCience and Engineering (CASE) 2021 Aug 23 (pp. 956-961). IEEE. [Link]

Acute Care Learning Laboratory – Reducing Threats to Diagnostic Fidelity in Critical Illness

Funding Agency: Agency for Healthcare Research and Quality (AHRQ) Grant Number: 1R18HS026609-01 Mayo Clinic PI: Brian Pickering UF PI: Xiang Zhong

Project Description: This project combines mixed-methods research approaches with systems engineering research approaches to understand the interplay of the multiple factors contributing to diagnostic error and delay. Dr. Zhong is the systems engineer in the team and will work collaboratively with Mayo Clinic physicians from the Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC) Lab to develop a “Control Tower” that will be used within the learning laboratory to inform the design, development, evaluation, and refinement of the solutions to diagnostic error and delay. The interventions developed through “Control Tower” have the potential to be shared with multiple practices and adapted to a variety of clinical environments.

Project Highlights:

Publications:

  • Park J, Zhong X, Dong Y, Barwise A, Pickering BW. Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach. BMC anesthesiology. 2022 Jan 4;22(1):10. [Link]
  • Zhong X, Babaie Sarijaloo F, Prakash A, Park J, Huang C, Barwise A, Herasevich V, Gajic O, Pickering B, Dong Y. A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units. International Journal of Production Research. 2022 Jul 3;60(13):4197-213. [Link]

Data Analytics for AKI & CHF Risk Prediction and Intervention

Funding agency: Baxter Healthcare Corporation UW-Madison PI: Jingshan Li UF PI: Xiang Zhong

Project Description: This project is aimed to developing data analytic methods to provide risk prediction and patient‐centered intervention strategies for acute kidney injury (AKI) and congestive heart failure (CHF) patients. Dr. Zhong is the UF PI in the team and will work collaboratively with UF Health and Gainesville VA physicians on identifying the risk factors for early diagnosis of CHF. The successful completion of the project will provide efficient and effective risk prediction models for AKI and CHF and individualized patient care plan for physicians and health practitioners. It can also help design and deployment of medical devices targeting at AKI and CHF monitoring and alarming. Moreover, such methodology could be extended to risk prediction and intervention of other critical diseases.

Project Highlights:

Publications:

  • Park J, Zhong X, Babaie Sarijaloo F, Wokhlu A. Tailored risk assessment of 90‐day acute heart failure readmission or all‐cause death to heart failure with preserved versus reduced ejection fraction. Clinical cardiology. 2022 Apr;45(4):370-8. [Link]
  • Sarijaloo F, Park J, Zhong X, Wokhlu A. Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis. Clinical cardiology. 2021 Feb;44(2):230-7. [Link]