Towards Effective Patient Simulators

Front Artif Intell. 2021 Dec 15:4:798659. doi: 10.3389/frai.2021.798659. eCollection 2021.


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.

Keywords: clinical methods; healthcare; markov decision chain; reinforcemenet learning; simulators and models.