Delay-Induced Uncertainty in Physiological Systems

Karamched, Bhargav R.; Albers, David J.; Hripcsak, George; Ott, William

doi:10.1101/2020.07.17.209544

Cited by 2 publications

(3 citation statements)

References 82 publications

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“…Previous theoretical research using dynamic models has shown that time dependent variations in insulin resistance and feeding patterns common to ICU patients can result in complex glucose responses. 32 , 50 , 53 This further highlights the importance of quantifying uncertainty.…”

Section: Discussionmentioning

confidence: 91%

“… 9 As assessed using CEGA, the majority of predictions (97.2%) will lead to a clinically appropriate response. The use of prediction intervals is a strength, with quantification of uncertainty important for glycemic prediction due to potentially complex dynamics decreasing point prediction performance 50 and the presence of measurement error. 23 …”

Section: Discussionmentioning

confidence: 99%

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Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Fitzgerald

Perez‐Concha

Gallego

et al. 2021

Journal of the American Medical Informatics Association

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Objective Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. Materials and Methods Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. Results Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%. Discussion ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. Conclusion We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Fitzgerald

Perez‐Concha

Gallego

et al. 2021

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

show abstract

“…Mathematical models with delays have served profoundly useful in capturing the behaviour of complex systems in biology [1][2][3][4][5][6][7], networks [8] and control [9]. One notable example is delayed negative feedback control of genetic networks, especially transcriptional feedback [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Stochastic switching of delayed feedback suppresses oscillations in genetic regulatory systems

Karamched

Miles

2023

J. R. Soc. Interface.

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Delays and stochasticity have both served as crucially valuable ingredients in mathematical descriptions of control, physical and biological systems. In this work, we investigate how explicitly dynamical stochasticity in delays modulates the effect of delayed feedback. To do so, we consider a hybrid model where stochastic delays evolve by a continuous-time Markov chain, and between switching events, the system of interest evolves via a deterministic delay equation. Our main contribution is the calculation of an effective delay equation in the fast switching limit. This effective equation maintains the influence of all subsystem delays and cannot be replaced with a single effective delay. To illustrate the relevance of this calculation, we investigate a simple model of stochastically switching delayed feedback motivated by gene regulation. We show that sufficiently fast switching between two oscillatory subsystems can yield stable dynamics.

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Delay-Induced Uncertainty in Physiological Systems

Cited by 2 publications

References 82 publications

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Stochastic switching of delayed feedback suppresses oscillations in genetic regulatory systems

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