Project description
A predictive model for endometriosis
Healthcare tools for predicting and preventing diseases as well as personalising treatment and patient management offer great clinical benefits and cost reduction. The EU-funded FEMaLe project is working on a machine-learning multi-omics platform that can analyse omics data sets and feed the information into a personalised predictive model. The main focus of the project is to improve intervention for individuals with endometriosis, a condition where tissue normally lining the uterus grows outside the uterus. A combination of tools such as a mobile application and augmented reality surgery software will be developed, facilitating improved disease management and the delivery of precision medicine.
Objective
The framework 'P4 Medicine' (predictive, preventative, personalized, participatory) was developed to detect and prevent disease through close monitoring, deep statistical analysis, biomarker testing, and patient health coaching to best use the limited healthcare resources and produce maximum benefit for all patients. However, we have seen only few feasible examples over the past 10 years.
The Finding Endometriosis using Machine Learning (FEMaLe) project will revitalise the concept to develop and demonstrate the Scalable Multi-Omics Platform (SMOP) that converts multi-omic person population datasets into a personalised predictive model to improve intervention along the continuum of care for people with endometriosis. We will design, validate and implement a comprehensive model for the detection and management of people with endometriosis to facilitate shared decision making between the patient and the healthcare provider, enable the delivery of precision medicine, and drive new discoveries in endometriosis treatment to deliver novel therapies and improve quality of life for patients.
We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver: 1) mobile health app for people with endometriosis,
2) three clinical decision support (CDS) tools for targeted healthcare providers (risk stratification tool for general practitioners, multi-marker signature tool for gynaecologists, and non-invasive diagnostic tool for radiologist), and
3) computer vision-based software tool for real time augmented reality guided surgery of endometriosis.
Health maintenance organisations (HMO) expect to be able to reduce overall cost of treatment by at least 20%, while improving patient outcomes, using CDS tools. The SMOP will be based on open protocol, embedded in all ethical and legal frameworks, to enable tailored and personalised usage to improve the lives of patients across Europe beyond the project period.
Fields of science
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
8000 Aarhus C
Denmark