This project will create an engineering-driven approach for use in development of comprehensive, automated treatment of critically ill patients suffering from hypovolemia (low blood volume). The research will focus on developing methods to monitor the patient?s overall blood volume state, creation of a mathematical model of the patient?s blood system, developing an algorithm to guide treatment decisions, and a system of controllers for treatment. The outcomes from this project will advance national health and quality of care of hypovolemic patients by improving treatment effectiveness as well as reducing healthcare costs. More generally, it will further the progress of basic science related to the automation of healthcare. This project will also support STEM education and inclusion of diversity through its unique education and outreach activities to attract under-represented groups to research.

Introduction of closed-loop automation to the care of critically ill patients suffering from hypovolemia has potential to overcome challenges related to patient safety and treatment effectiveness. However, existing effort has mostly resorted to ad-hoc control techniques in the absence of control-oriented mathematical models and systematic model-based tools for real-time patient monitoring and closed-loop treatment control. To address these challenges, this project seeks to develop a suite of methods and tools to enable closed-loop automation of hypovolemia treatment, including an array of control-oriented models of critically ill patients under hypovolemia, disciplined procedures for system identification and analysis of such models, novel techniques for real-time monitoring of a patient's blood volume state leveraging such models, and systematic methods for the design of model-based closed-loop hypovolemia treatment controllers. This project will advance a wide spectrum of aspects related to closed-loop control in medicine, ultimately contributing to improved quality of life of human beings.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2018-09-15
Budget End
2021-08-31
Support Year
Fiscal Year
2017
Total Cost
$346,342
Indirect Cost
Name
University of Maryland College Park
Department
Type
DUNS #
City
College Park
State
MD
Country
United States
Zip Code
20742