DeepAI AI Chat
Log In Sign Up

Graph representation forecasting of patient's medical conditions: towards a digital twin

09/17/2020
by   Pietro Barbiero, et al.
0

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.

READ FULL TEXT

page 13

page 14

page 15

06/09/2020

The Computational Patient has Diabetes and a COVID

Medicine is moving from a curative discipline to a preventative discipli...
05/31/2022

A review of machine learning approaches, challenges and prospects for computational tumor pathology

Computational pathology is part of precision oncology medicine. The inte...
06/14/2018

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories

In many forecasting applications, it is valuable to predict not only the...
12/04/2019

Deep Physiological State Space Model for Clinical Forecasting

Clinical forecasting based on electronic medical records (EMR) can uncov...