SOURCE-LINKED INTELLIGENCE

PERSONALIZED REHABILITATION VIA NOVEL AI PATIENT STRATIFICATION STRATEGIES

CORDIS · observation · Publication date unknown

hese 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, a

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recordType

award

status

SIGNED

region

EU

value

5774112.5

unit

EUR