📋 Tender Overview
Personalized Rehabilitation Via Novel Ai Patient Stratification Strategies
Ai Tech And Methods For Rehabilitation Patient Stratificationproviding Rehabilitation Care For Patients With Chronic, Especially Non-Communicable Diseases, Can Be Exceedingly Complex And Challenging. The Highly Personalised Nature Of Each Patient’S Condition Makes Prediction Tools And Patient Stratification Unreliable. Despite Efforts Using Big Data, Existing Datasets Often Give Rise To New Challenges. The Eu-Funded Prepare Project Aims To Address These Challenges Through A Combination Of Innovative Clinical, Socio-Behavioural, And Public Health Research, Coupled With Advanced Ai And Data Science Methods. Additionally, They Will Employ Machine Learning Techniques On Large-Scale, Comprehensive Patient Datasets To Stratify Patients And Identify New Predictors For Rehabilitation Outcomes. Ultimately, The Project Will Culminate In The Development Of A Platform To Showcase Their Findings And Roadmap.
Funded Under
Health
Total Cost
€ 5 774 112,50
Eu Contribution
€ 5 774 112,50
Coordinated By
Aristotelio Panepistimio Thessalonikis
Greece
Objective
PREPARE aims at advancing rehabilitation care for patients with chronic non-communicable diseases. As rehabilitation is a complex, multifaceted, and highly personal process, we currently lack reliable patient stratification and outcome prediction tools. While big data approaches provide a path forward, existing data sets pose numerous challenges. These challenges can be overcome by combining advances in clinical research, socio-behavioral and public health research, data science, and advanced statistical and AI learning methods.
We will apply machine learning techniques on our large-scale patient data sets including key sociodemographic, living conditions, and behavioral information to stratify patients based on expected outcomes. A subsequent analysis will consider all potential predictors for rehabilitation outcome. Baseline strata and modifiers will be used to develop a comprehensive model of each clinical situation to increase management quality, improve outcomes, and reduce costs.
As proof of principle we will develop a platform for sharing model results, exploiting the open-science EHDEN platform, and showcase the novel approach through pilot cases of nine pathologies which constitute the most dominant causes for rehabilitation worldwide: hand disorders, hip and knee prosthesis, intermittent claudication, lower limb loss, Parkinson’s disease/Parkinsonisms, scoliosis, spine disorders, temporo-mandibular articulation, and hypertension. We will also develop a certification roadmap.
PREPARE will result in innovative, robust, personalized, and validated data-driven computational prediction and stratification tools to support healthcare professionals and patients in selecting the optimal therapy strategy
