Iannis is a data and informatics scientist (BSc, MSc, 2000, School of Engineering) with a PhD in Medicine and Machine Learning (“Prognosis, diagnosis and treatment of malignant lymphomas using Artificial Intelligence”, 2009, School of Medicine). As a pioneer of precision medicine, his work is focusing around biomedical Big Data integration, analysis and Artificial Intelligence (AI) predictions.

He has successfully served via senior roles numerous national, European and international research projects, clinical trials and educational activities. His work includes 1st time solutions to open problems, contribution to open-source projects and using AI (machine and deep learning) to translate health data into improved healthcare services.

Iannis has been teaching data science to under- and post-graduate medical students for more than 10 years, with his book chapters on health databases (Efficient Database Design) and cloud computing (Web Delivered Interactive Applications) being part of the teaching portfolio of US Medical Schools.

Past projects where Iannis was involved were aiming to improve data integration (mainly clinical/phenotypic with OMICs), cross-analysis, and AI driven biomarker discovery.

Currently, he is heading Big Data and Personalized Medicine at the Center for Surgical Science (CSS) in Zealand’s University Hospital, Denmark. At CSS, Personalized Medicine data driven development is relying on the OMOP CDM and the OHDSI opensource tools to unify heterogeneous data and unleash the full power of AI for the complete spectrum of health data. The common target of all Personalized Medicine CSS projects is to translate health data into improved diagnosis, prognosis and treatment for the benefit of the patient and the healthcare professional.

  • Clinical and multi-omics cross-phenotyping of patients with autoimmune and autoinflammatory diseases: the observational TRANSIMMUNOM protocol. Lorenzon R, Mariotti-Ferrandiz E, Aheng C, Ribet C, Toumi F, Pitoiset F, Chaara W, Derian N, Johanet C, Drakos I, Harris S, Amselem S, Berenbaum F, Benveniste O, Bodaghi B, Cacoub P, Grateau G, Amouyal C, Hartemann A, Saadoun D, Sellam J, Seksik P, Sokol H, Salem JE, Vicaut E, Six A, Rosenzwajg M, Bernard C, Klatzmann D. BMJ Open 2018;8:e021037. doi: 10.1136/bmjopen-2017-021037.
  • Web Delivered Interactive Applications. Drakos J. Informatics in Medical Imaging. Published October 17th, 2011 by CRC Press, USA.
  • Efficient Database Design. Drakos J. Informatics in Medical Imaging. Published October 17th, 2011 by CRC Press, USA.
  • Bayesian clustering of flow cytometry data for the diagnosis of B-Chronic Lymphocytic Leukemia. Lakoumentas, J. Drakos, J. Karakantza, M. Nikiforidis, G. Sakellaropoulos, G. Journal of Biomedical Informatics, 2009;42:251-261.
  • A perspective for biomedical data integration: Design of databases for Flow Cytometry. Drakos, J. Karakantza, M. Zoumbos, N. Lakoumentas, J. Nikiforidis, G. Sakellaropoulos, G. BMC Bioinformatics (Volume 9, 14/2/2008, article number 99).
LAST UPDATE: MAR 2020