FoundationEHR is a transformer-based deep neural network architecture pre-trained on almost 1 billion EHRs from ~3.5 million patients in the USA

Initial situation and context

High potential for the use of structured electronic health records (EHRs)
Structured Electronic Health Records (EHRs) comprising diagnoses, prescriptions, and procedure codes have widespread use in many healthcare systems. They are, e.g., utilized for health-economic, pharmaco-epidemiological, pharmaco- vigilance, and marketing purposes. Furthermore, there is an increasing interest in using these data for personalized risk prediction, understanding real-world disease trajectories, constructing external control arms, or emulating entire clinical trials. In this context, artificial intelligence (AI) and, specifically, machine learning (ML) are employed more and more.

Example workflow for the use of FoundationEHR

Existing challenges

Structured EHR data is initially not ready to apply statistical modeling techniques or advanced AI / ML. Traditionally, many researchers have constructed an analysis dataset by simply indicating the presence/absence of specific diagnoses before a defined index date in a patient’s history.
This approach ignores any temporal aspects of disease development and is often biased towards a priori-defined diagnosis, preventing the discovery of new insights from data.

Developed by

Fraunhofer SCAI

Your contact person

I will be happy to provide you with information about our software products.

Bettina Keller
Bettina KellerProduct sales

Leverage the potential of EHRs via deep learning

We have developed a modern deep learning-based approach to deal with structured EHR data, which leverages the full potential of these data. FoundationEHR is a transformer- based deep neural network architecture pre-trained on almost 1 billion EHRs from ~3.5 million patients in the USA. FoundationEHR is a foundation model, i.e., an AI model, from which the customer can derive further task-specific AI/ML models in a further fine-tuning (transfer-learning) procedure. FoundationEHR distinguishes itself from other solutions through multiple aspects:

  • FoundationEHR can model prescription information in addition to diagnoses.

  • FoundationEHR models information about a patient’s age at each doctor visit. Furthermore, information about which diagnosis and prescriptions belong to the same encounter is correctly modeled.

  • FoundationEHR models information about a patient’s residency and gender.

  • FoundationEHR can integrate quantitative lab or imaging data in addition to qualitative EHRs.

  • Explanation of model predictions.