This repository contains the implementation of a "digital twin" model of patients, i.e. a general framework that composes advanced AI approaches and integrates mathematical modelling in order to provide a panoramic view over current and future physiological conditions of patients.
Objective: Modern medicine needs to shift from a wait and react , curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients. The aim of this work is to present how the integration of machine learning approaches with mechanistic computational modelling could yield a reliable infrastructure to run probabilistic simulations where the entire organism is considered as a whole.
Methods: We propose a general framework that composes advanced AI approaches and integrates mathematical modelling in order to provide a panoramic view over current and future physiological conditions. The proposed architecture is based on a graph neural network (GNNs) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GANs) providing a proof of concept of transcriptomic integrability.
Results: We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data to drive local and global clinical parameters and derive future trajectories representing the evolution of the physiological state of the patient.
Significance: We argue that the graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modelling with AI. We believe that this work represents a step forward towards a healthcare digital twin.
If you find this repository useful in your research, please consider citing the following papers.
Digital patient:
@article{barbiero2020graph,
title={Graph representation forecasting of patient's medical conditions: towards a digital twin},
author={Barbiero, Ramon Vi\~nas Torn\'e, Pietro Li\'o},
journal={arXiv preprint},
year={2020}
}
Computational patient:
@article{barbiero2020computational,
title={The Computational Patient has Diabetes and a COVID},
author={Barbiero, Pietro and Li{\'o}, Pietro},
journal={arXiv preprint arXiv:2006.06435},
year={2020}
}
The model can be used to actively monitor and forecast clinical endpoints predicting the evolution of patient's conditions.
Pietro Barbiero, Ramon Viñas Torné and Pietro Liò.
Copyright 2020 Pietro Barbiero, Ramon Viñas Torné and Pietro Liò.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.