<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/ACF0115E-1B1F-42D7-8C2C-D2DA10A103E5" ns1:id="ACF0115E-1B1F-42D7-8C2C-D2DA10A103E5"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/DF5B3226-3940-4FE4-8E93-5801F3B86B6B" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/C2ACE240-ED42-4360-8D94-111FAE6CD65E" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/C2ACE240-ED42-4360-8D94-111FAE6CD65E" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2027-05-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/E0A1BE63-0A33-4813-8A93-353AD8C82D6C" ns1:rel="FUND" ns1:start="2023-05-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10086219</ns2:identifier></ns2:identifiers><ns2:title>PREPARE: PERSONALIZED REHABILITATION VIA NOVEL AI PATIENT STRATIFICATION STRATEGIES</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>EU-Funded</ns2:grantCategory><ns2:leadFunder>Horizon Europe Guarantee</ns2:leadFunder><ns2:abstractText>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.</ns2:abstractText></ns2:project>