Data Science Projects

One in four patients experience complications after bowel cancer surgery, leading to re-admission, lasting harm and in some cases early death. A new research project, called FLORENCE, aims to improve the diagnosis, prognosis and treatment of patients with bowel cancer. The project will develop an Artificial Intelligence tool to provide the doctors with an improved basis for decisions regarding the treatment of patients.

In addition to AI, the FLORENCE project uses the OMOP Common Data Model,  which is one of the leading approaches to creating data infrastructures that facilitate the use of personalised medicine in medical care.

For the first time at a global level, the project will link the AI model directly to the clinic through federated learning. Therefore, the project is unique in its approach to solve a healthcare challenge for the patient population.

The project’s lead partner is the Center for Surgical Science and the Research Department at the University Hospital of Zealand in Køge. Partners are Oslo University Hospital (respectively the Oncological Pelvic Surgery Unit and the Cancer Registry of Norway), Lund University and the Technical University of Denmark (DTU). The Netherlands Cancer Registry (IKNL) is also participating in the project.

FLORENCE is an abbreviation for the central activities in the project (federated learning using OMOP-modelling of register data to improve treatment of bowel cancer in the Nordics).

One of the main targets of Personalized Medicine is to refine the stratification of a single patient aiming to provide improved diagnosis, prognosis, and treatment for the specific individual. This can be achieved by including not only a detailed account of the patient’s health record, but also similar data from population-wide databases and clinical projects. These multiple data sources can be collected in a single and common data model to serve as a powerful platform. The model will include the necessary data science domains such as machine learning and aid the clinician in the decision-making process. To facilitate the implementation, real-time data from the electronic health report of the patient is needed. We will, in collaboration with data scientists and medical and biological professionals, establish the necessary infrastructure to promote this model in a clinical use case and finally, expand it within other medical fields. 

The project is funded by the Novo Nordisk Foundation.