Patient simulation. Generation of a machine learning “inverse” digital twin.

In the medtech industry models of the cardiiovascular systems and simulations are valuable tools for the development of new products ad therapies. The simulator Aplysia has been developed over several decade and is able to replicate a wide range of phenomena involved in the physiology and pathophysiology of breathing and circulation. Aplysia is also able to simulate the hemodynamics phenomena starting from a set of patient model parameters enhancing the idea of a "digital twin", i.e. a patient-specific representative simulation. Having a good starting estimate of the patient model parameters is a crucial aspect to start the simulation. A first estimate can be given by looking at patient monitoring data but medical expertise is required. The goal of this thesis is to address the parameter estimation task by developing machine learning and deep learning model to give an estimate of the patient model parameter starting from a set of time-varying data that we will refers as state variables. Those state variables are descriptive of a specific patient and for our project we will generate them through Aplysia starting from the simulation presets already available in the framework. Those presets simulates different physiologies, from healthy cases to different cardiovascular diseases. The thesis propose a comparison between a machine learning pipeline and more complex deep learning architecture to simultaneously predicting all the model parameters. This task is referred as Multi Target Regression (MTR) so the performances will be assessed in terms of MTR performance metrics. The results shows that a gradient boosting regressor with a regressor- stacking approach achieve overall good performances, still it shows some lack of performances on some target model parameters. The deep learning architectures did not produced any valuable results because of the amount of our data: to deploy deep architectures such as ResNet or more complex Convolutional Neural Network (CNN) we need more simulations then the one that were done for this thesis work.