Concluded

Digital twin modelling of patients with cognitive disorders

Duration

December 2019 – June 2023

Funding program

National Scientific Fund, Bulgarian Ministry of Education and Science

Project Type

Collaborative Research & Innovation Action

Coordinator

Assoc. prof. Dessislava Petrova-Antonova

Goals & Impact

Aim

The aim of the project is to create digital twin models based on big data and machine learning and artificial intelligence methods for exploration of behavioural changes in patients with proven cognitive disorders. Such models will identify the causes and trends that lead to a deepening of the long-term cognitive decline. Their results will be used to assess cognitive status in a timely manner and suggest appropriate preventive actions of cognitive disorders.

Research

The digital twin implementation includes the following stages:

Create – The create stage includes the building of a Patient Information Model (PIM) by collecting data from different sources. The data can be classified into two categories: (1) operational data related to results from cognitive tests; and (2) external data related to the patient cognitive status. The data may be augmented with information for other patients with a similar diagnosis.
Interact – The interact stage provides near real-time, seamless, bidirectional connectivity between the physical patient and its digital twin (virtual patient). The communication infrastructure transmits the collected information to the digital platform.
Aggregate – The aggregate stage covers data ingestion into a data storage and data pre-processing such as cleaning, consistency checking, linking, etc. It could be performed on the premises or in the cloud.
Analyze – The analyze stage is based on a variety of models of virtual patients that are built on top of PIM. The main goal of this stage is to produce new knowledge that drives the decision-making process. Artificial intelligent methods and cognitive computing are applicable at that stage.
Insight – The insight stage visualizes the insights from analytics as 3D views, dashboards and others. It aims to provide evidence about areas for further investigations.
Decision – The decision stage applies the new knowledge to the therapy of the physical patient in order to produce an impact of the digital twin. The decisions lead to precise diagnosis and timely and adequate therapy.