Research Grants

Research Grants


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.

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.

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.

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.