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ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 |
doi: 10.3389/fphys.2024.1424931
This article is part of the Research Topic Artificial Intelligence for Smart Health: Learning, Simulation, and Optimization View all 7 articles
Model-driven Engineering for Digital Twins: A Graph Model-based Patient Simulation Application
Provisionally accepted- 1 University of Florida, Gainesville, Florida, United States
- 2 Mayo Clinic, Rochester, Minnesota, United States
Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 hours of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform front-end application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.
Keywords: Digital Twin, Virtual Patient Simulation, Graph model, Full-Stack application architecture, Critical Care
Received: 28 Apr 2024; Accepted: 19 Jul 2024.
Copyright: © 2024 Trevena, Zhong, Lal, Rovati, Cubro, Dong, Schulte and Gajic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiang Zhong, University of Florida, Gainesville, 32609, Florida, United States
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