A regularized system identification approach to subject-specific physiological modeling with limited data

Ali Tivay, Ghazal Arabi Darreh Dor, Ramin Bighamian, George C. Kramer, Jin Oh Hahn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper investigates a novel regularized system identification approach to physiological modeling using limited data. The proposed approach operates in two steps: 1) limited data from individual subjects are consolidated and leveraged to determine a population-average physiological model; then, 2) a subject-specific model for an individual subject is derived from a regularized system identification procedure whose objective is to reconcile the model's capability to predict individual-specific behavior and to retain typical population-representative trends. This is achieved by embedding a regularizing condition into the cost function for system identification that enforces parsimony in parametric deviation from the population-average model. A few unique advantages of the proposed approach are that 1) it offers superior predictive accuracy in both measured as well as unmeasured physiological system responses when compared to a standard system identification approach; and 2) it provides high-sensitivity parameters in the model associated with each individual subject, thus potentially eliminating the necessity for post-hoc parametric sensitivity analysis. Merits and limitations of the proposed regularized approach are illustrated with a real world case study on physiological modeling of hemodynamics in response to burn injury and resuscitation.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3468-3473
Number of pages6
ISBN (Electronic)9781538679265
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period7/10/197/12/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Tivay, A., Darreh Dor, G. A., Bighamian, R., Kramer, G. C., & Hahn, J. O. (2019). A regularized system identification approach to subject-specific physiological modeling with limited data. In 2019 American Control Conference, ACC 2019 (pp. 3468-3473). [8815199] (Proceedings of the American Control Conference; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc..