Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis

An, Gary; Cockrell, Chase

doi:10.1007/978-1-0716-1488-4_20

Cited by 2 publications

(8 citation statements)

References 38 publications

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Section: Discussionmentioning

confidence: 99%

“…Although critical care technologies have improved, the prevalence and fatality rates of sepsis are still increasing ( 1 , 2 ). A frequent complication of sepsis is the development of organ dysfunction ( 1 , 2 ). Platelets are implicated in endothelial damage and take part in the pathogenesis of tissue damage in sepsis.…”

Section: Discussionmentioning

confidence: 99%

“…Sepsis involves multiple processes, including inflammation, immunity, coagulation and neuroendocrine responses. The pathophysiological mechanism is complex and the clinical manifestations of patients vary ( 2 ). Consequently, there is a lack of rapid, sensitive and specific diagnostic biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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Platelet‑related parameters as potential biomarkers for the prognosis of sepsis

et al. 2023

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Early diagnosis and accurate prognosis are key for reducing the fatality rate and medical expenses associated with sepsis. Platelets are involved in the delayed tissue injury that occurs during sepsis. Therefore, the aim of the present study was to investigate the usefulness of platelets and associated parameters as prognostic markers of sepsis. The present study collected patient samples based on The Third International Consensus Definitions for Sepsis and Septic Shock criteria. Platelet-associated parameters were detected by flow cytometry and their correlation with clinical scores and prognoses was analyzed. Considering the association between endothelial cells and platelet activation, levels of plasma tumor necrosis factor-like weak inducer of apoptosis (TWEAK) and angiopoietin-2 (Ang-2) were analyzed by ELISA. The results showed significant differences in platelet P-selectin expression and phosphatidylserine exposure, mitochondrial membrane potential (Mmp)-Index values and plasma levels of TWEAK and Ang-2 between patients and healthy controls (P<0.05). Except for P-selectin and TWEAK levels, all parameters were correlated with clinical scores (acute physiology and chronic health evaluation II and sequential/sepsis-related organ failure assessment). Additionally, platelet Mmp-Index between admission and the end of therapy was only different in non-survivors (P<0.001) and platelet phosphatidylserine exposure was significantly lower in survivors (P=0.006). Therefore, of the parameters tested, the dynamic monitoring of phosphatidylserine exposure, platelet Mmp-Index values and plasma Ang-2 levels had the most potential for the assessment of disease severity and clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Platelet‑related parameters as potential biomarkers for the prognosis of sepsis

et al. 2023

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“…We train a neural ODE metamodel on ABM instances that are based on the same values of substrate inflow as in the two other metamodels (see Materials and Methods for further details). We then used the trained neural ODE model to determine the optimal substrate inflow that minimizes the loss J 2 (q) [see (5)]. The neural ODE identifies an optimal substrate inflow of 0.7, which coincides with the optimum of the ABM.…”

Section: Neural Ode Metamodel and Controllermentioning

confidence: 99%

“…The ANN associated with q k (θ) consists of five fully connected hidden layers with five exponential linear units (ELU) each. We train the neural ODE controller using the loss function defined in (5). Subsequently, we use the trained controller as input in the metabolic pathway ABM.…”

Section: Mechanistic Approach (Predator-prey Dynamics)mentioning

confidence: 99%

Control of Medical Digital Twins with Artificial Neural Networks

Böttcher,

Fonseca,

Laubenbacher

2024

Preprint

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The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

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Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis

Cited by 2 publications

References 38 publications

Platelet‑related parameters as potential biomarkers for the prognosis of sepsis

Platelet‑related parameters as potential biomarkers for the prognosis of sepsis

Control of Medical Digital Twins with Artificial Neural Networks

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