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.





  • Alepopoulos Vasilis
  • Grammatikopoulou Margarita
  • Kompatsiaris Yiannis (Ioannis)
  • Nikolopoulos Spiros