• Posted 08-Feb-2021


The collaborative ALAMEDA project aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions.

The collaborative ALAMEDA project aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions.

The University of Nicosia (UNIC) is a partner in the ALAMEDA project, alongside 14 other partners from 8 different European countries, committed to researching and prototyping the next generation of personalised 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).

“Through its Artificial Intelligence Lab, UNIC will greatly contribute to ALAMEDA’s Artificial Intelligence, Machine Learning and Natural Language Processing elements. More specifically, one of the major innovations that we will develop is a conversational agent (Chat-bot) that will interact with patients and collect information in a non-intrusive way”, said Dr. Ioannis Katakis, Project Coordinator for UNIC. Dr. Katakis is an Associate Professor at the University of Nicosia, Computer Science Department, and co-founder and co-director of the Artificial Intelligence Lab.

About the Project

New opportunities for improved personalised healthcare and prevention have been enabled by the recent advances in the design and development of innovative health risk prediction and intervention tools: digital transformation is a challenging necessity due to the global healthcare workforce shortage that is believed to reach a deficit of about 4.1 million skilled health professionals (midwives, nurses and physicians) by 2030 in the EU, according to the World Health Organization. In the case of brain diseases research, technological advances have proved particularly effective. Big Data Analytics and Machine Learning methods can provide clinically actionable information that can complement medical recommendations and foster better treatments. This is particularly important because neurological disorders account for an increasing burden in terms of disability-adjusted life-years (DALYs) (i.e. the number of years lost due to ill-health, disability or early death), ranking third after cancer and cardiovascular diseases.

ALAMEDA acknowledges that the care of patients with brain disorders is complex and that manifestations of certain diseases could worsen over time and seriously impair the quality of life of patients and their caregivers: regular rehabilitation treatment assessments are essential to ensure that:

  • medical interventions are impactful; and
  • relapse incidents can be foreseen.

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 personalised data recordings, that could include both pharmacological and non-pharmacological therapeutic options, such as exercise regimens. The application of digital technologies to specific healthcare issues and chronic conditions has the potential to generate rich diagnostic data streams. Big Data management and AI methods are then applied on these data streams to disaggregate them into meaningful information components that provide smart personalised healthcare guidance, underpinning existing practices and medical protocols. In the years to come, ongoing research is expected to bring unprecedented progresses through risk prediction tools, modeling and improved disease understanding.

The use of AI methods (Big Data Analytics, Machine and Deep Learning) as predictive tools is particularly relevant for brain diseases as, in many cases, by the time all the clinical symptoms manifest, so that specialists can make a definitive diagnosis, the outcomes are essentially irreversible.  In this light, better tools for assisting the detection of early signs of brain disease are needed. Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, identify anomalies in “expected” patterns and associate similar patients/diseases/drugs based on common features. In healthcare, it is expected that Deep Learning will bring a disruption, paving the way to a paradigm shift in Clinical Decision Support Systems (CDSSs), diagnosis and treatment selection. This change is further fueled by the recent advances in the digitalisation of healthcare records, including assets such as medical reports, imagery, or sensory data.

The Partnership

The European collaborative project kicked off in January 2021 and will run for 36 months. It receives funding (6 million Euro) from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement No GA101017558. The project consortium comprises 15 partners from 8 different European countries: Institute Of Communication and Computer Systems (ICCS); Ethniko Kai Kapodistriako Panepistimio Athinon (NKUA); Ethniko Kentro Erevnas Kai Technologikis Anaptyxis (CERTH) and Enora Innovation Etaireia Psifiakon Texnologion Kai Ergon Kainotomias Idiotiki Kefalaiouxiki Etaireia (ENO) from GREECE; Wellics Ltd (WCS) from UNITED KINGDOM; EY Advisory SPA (EY), Fondazione Italiana Sclerosi Multipla Onlus (FISM) and Pluribus One Srl (PLU) from ITALY; Universitatea Politehnica Din Bucuresti (UPB) and Spitalul Universitar De Urgenta Bucuresti (SUUB) from ROMANIA; Norges Teknisk-Naturvitenskapelige Universitet (NTNU) from NORWAY; Unisystems Luxemburg Sarl (UNISYS) from LUXEMBURG; Wise Angle Consulting SL (WISE) from SPAIN; Catalink Limited (CTL) and University of Nicosia (UNIC) from CYPRUS.

Project Contact for UNIC: Dr. Ioannis Katakis, Associate Professor, School of Sciences and Engineering Department of Computer Science, Email: [email protected]

Source: University of Nicosia (https://bit.ly/36TD2np)