Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery

William Trevena, Amos Lal, Simon Zec, Edin Cubro, Xiang Zhong, Yue Dong, Ognjen Gajic

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalIEEE Robotics and Automation Letters
DOIs
StateAccepted/In press - 2022

Keywords

  • Computational modeling
  • Critical care
  • Databases
  • Digital simulation
  • Digital twins
  • Graph model
  • Kidney
  • Medical conditions
  • Medical diagnostic imaging
  • Modeling
  • Patient pathway

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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