Towards Effective Patient Simulators

Vadim Liventsev*, Aki Härmä, Milan Petković

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this paper we give an overview of the field of patient simulators and provide qualitative and quantitative comparison of different modeling and simulation approaches. Simulators can be used to train human caregivers but also to develop and optimize algorithms for clinical decision support applications and test and validate interventions. In this paper we introduce three novel patient simulators with different levels of representational accuracy: HeartPole, a simplistic transparent rule-based system, GraphSim, a graph-based model trained on intensive care data, and Auto-ALS—an adjusted version of an educational software package used for training junior healthcare professionals. We provide a qualitative and quantitative comparison of the previously existing as well as proposed simulators.

Original languageEnglish
Article number798659
JournalFrontiers in artificial intelligence
Volume4
DOIs
Publication statusPublished - 15 Dec 2021
Externally publishedYes

Keywords

  • clinical methods
  • healthcare
  • markov decision chain
  • reinforcemenet learning
  • simulators and models

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