ALAMEDA

Bridging the Early Diagnosis and Treatment Gab of Brain Diseases via Smart, Connected, Proactive and Evidence-based Technological Interventions

The EU-funded ALAMEDA project aims to provide new opportunities for improved personalized healthcare and prevention that have been enabled by the recent advances in the design and development of Innovative health risk prediction and intervention tools. ALAMEDA’s vision is to research and prototype the next generation of personalized AI healthcare support systems for people with brain diseases and disorders, specifically focusing on the needs of patients with Parkinson’s, Multiple Sclerosis and Stroke (PMSS). The project’s innovations will take advantage of new machine learning models, built upon lifestyle retrospective data as well as new streams of patient data that involve the monitoring of everyday activities, such as sleep behaviour and emotional status.
The success of such applications will provide clinicians with the opportunity to modify interventions based on personalized data recordings, that could include both pharmacological and non-pharmacological therapeutic options, such as exercise regimens.
M4D/MKLab will coordinate activities relevant to Artificial Intelligence approaches, aiming the reinvention of the modern healthcare in order to reinforce personalized early risk prediction, prevention and intervention.

PUBLICATIONS

  1. Dimoudis D, Tsolakis N, Magga-Nteve C, Meditskos G, Vrochidis S, Kompatsiaris I. "InSEption: A Robust Mechanism for Predicting FoG Episodes in PD Patients". Electronics. 2023; 12(9):2088. https://doi.org/10.3390/electronics12092088
  2. N. Tsolakis, C. Maga-Nteve, G. Meditskos, S. Vrochidis and I. Kompatsiaris, "Ince-PD Model for Parkinson’s Disease Prediction Using MDS-UPDRS I & II and PDQ-8 Score" in Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology,  Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds), vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_23
  3. Triantafyllidis A, Segkouli S, Zygouris S, Michailidou C, Avgerinakis K, Fappa E, Vassiliades S, Bougea A, Papagiannakis N, Katakis I, Mathioudis E, Sorici A, Bajenaru L, Tageo V, Camonita F, Magga-Nteve C, Vrochidis S, Pedullà L, Brichetto G, Tsakanikas P, Votis K, Tzovaras D. Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review. Sensors. 2023; 23(7):3396. https://doi.org/10.3390/s23073396
  4. C. Maga-Nteve, E. Kontopoulos, N. Tsolakis, P. Mitzias, I. Katakis, E. Mathioudis, K. Avgerinakis, G. Meditskos, A. Karakostas, S. Vrochidis and I. Kompatsiaris. (2021). A Semantic Technologies Toolkit for Bridging Early Diagnosis and Treatment in Brain Diseases – Report from the Ongoing EU-funded Research Project ALAMEDA. In Proceedings of Metadata and Semantics Research Conference, Springer, Madrid, 2021.
  5. C. Maga-Nteve, N. Tsolakis, G. Meditskos, A. Karakostas, S. Vrochidis and I. Kompatsiaris. (2021). "Ontology-based semantic model for health data interpretation", in Proceedings of Fourth International Workshop on Semantic Web Meets Health Data Management (SWH), the 20th International Semantic Web Conference (ISWC), Springer, Virtual Conference 24 - 28 October 2021.