Continuous stratification for improved prevention, treatment, and rehabilitation of stroke patients using digital twins and AI

State-of-the-art stratification today is based on machine-learning (ML) algorithms, trained on large cohort data.

This has two main limitations: a) such ML-models cannot use all the variety of different data that is generated about a patient, b) stratification is thus only done intermittently, implying out-dated and sub-optimal care decisions.

To remedy this, we herein present a new concept and technology - continuous stratification, using our new STRATIF-AI platform.

In continuous stratification, all data generated about a patient is cumulatively stored in a Personal Data Vault, controlled by the patient. These personal data continuously updates our world-unique digital twins.

The unique potential with our twins comes from the hybrid architecture, combining mechanistic, multi-scale, and multi-organ models with ML and bioinformatics.

This allows us to simulate patient-specific responses to changes in diet, exercise, and certain medications, and see changes on both an intracellular, organ, and whole-body level, ranging from seconds to years.

We also combine semantic harmonization with federated learning to securely re-train the various sub-models, when new data become available in one of the cohort databases.

In this project, we will for the first time use this cutting-edge technology to connect a series of apps that together covers an entire patient journey.

Using 6 new clinical studies, involving 8 new partner hospitals, we will both refine and validate the models, and demonstrate how the same digital twin can follow a patient across different apps, covering all phases of stroke: from prevention, to acute treatment, and rehabilitation.

Our scalable platform for continuous stratification forms the foundation for a new interconnected and patient-centric healthcare system.