Description du projet
Un modèle prédictif pour l’endométriose
Les outils de soins de santé destinés à prédire et à prévenir les maladies ainsi qu’à personnaliser le traitement et la prise en charge des patients présentent des avantages cliniques importants et permettent une réduction conséquente des coûts. Le projet FEMaLe, financé par l’UE, travaille sur une plateforme multi-omique en apprentissage automatique, capable d’analyser des ensembles de données omiques et d’alimenter un modèle prédictif personnalisé. Le principal objectif de ce projet est d’améliorer l’intervention pour les femmes atteintes d’endométriose, un trouble où le tissu qui tapisse normalement la paroi utérine se développe en dehors de l’utérus. Une combinaison d’outils tels qu’une application mobile et un logiciel de chirurgie en réalité augmentée sera mise au point, afin de faciliter une meilleure prise en charge de la maladie et d’ouvrir la voie à une médecine de précision.
Objectif
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.
Champ scientifique
Programme(s)
Régime de financement
RIA - Research and Innovation actionCoordinateur
8000 Aarhus C
Danemark