Descrizione del progetto
Un modello predittivo per l’endometriosi
Gli strumenti sanitari per prevedere e prevenire le malattie, nonché per personalizzare il trattamento e la gestione del paziente, offrono notevoli benefici clinici e riduzioni dei costi. Il progetto FEMaLe, finanziato dall’UE, sta lavorando a una piattaforma multi-omica di apprendimento automatico in grado di analizzare serie di dati omici e immettere informazioni in un modello predittivo personalizzato. L’obiettivo principale del progetto è migliorare il trattamento delle donne affette da endometriosi, una patologia in cui il tessuto che normalmente riveste l’utero cresce al di fuori di esso. Sarà realizzata una combinazione di strumenti, quali un’applicazione mobile e un software chirurgico basato sulla realtà aumentata, che agevolerà la gestione della malattia e consentirà di avvalersi della medicina di precisione.
Obiettivo
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.
Campo scientifico
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SC1-DTH-2020-1
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
8000 Aarhus C
Danimarca