Georgios Manias
A Computer Engineer with MSc in Big Data and Analytics. Received his B.Sc Diploma from Computer Engineering and Informatics Department, University of Patras, Greece, and his MSc in Big Data and Analytics from the Department of Digital Systems, University of Piraeus, Greece. An enthusiastic, adaptive and fast-learning person with a broad and acute interest in following a Data Scientist career. Main research interests are NLP, Machine Translation, Sentiment Analysis and Information Extraction. As a Senior Research Engineer of the University of Piraeus Research Centre (UPRC) of University of Piraeus, he has participated in several EU and National funded projects (e.g. CrowdHEALTH, Cybele, PolicyCLOUD, iHelp, etc) leading research for addressing issues related to quality of service provisioning, data interoperability, data governance, Natural Language Processing (NLP) in service oriented environments and application domains such as smart cities, finance, e-health and others.
Sessions
The successful exploration and interpretation of all the health-related data plays a vital role [1]. Healthcare data are available in different forms (e.g., images, signals, and wavelengths, etc.). All these data may come from different healthcare entities (i.e., patients themselves, healthcare professionals, etc.), where more and more entities place data demand on other entities, and many healthcare organizations find themselves overwhelmed with data, but lacking truly valuable information. At the same time, the use of electronic health records (EHRs) enables the management of patient data, for health care as well as other purposes, across any kind of institutional, regional, or national border. Data can thus be shared and used more effectively for quality assurance, disease surveillance, public health monitoring and research. Nonetheless, what is required refers to standardisation of data models to support interoperability between all the medical information systems that stored clinical information about patients. due to the improvement in the automatic collection of data from medical devices and systems, researchers and analysts can monitor data or information that can be accessed in electronic configuration [2]. On top of that, a crucial role in the huge expansion of the healthcare data play the wearable devices. To address these issues, many open-source analytics frameworks have developed large open ecosystems, with many contributing organizations, which is essential for achieving a federated analytics framework which can handle a broad and growing set of heterogeneous data sources [3]. Open source software is a promising way for healthcare organizations to reduce IT infrastructure costs while remaining flexible enough to adopt new IT solutions that will enable future improvements in patient care and business operations [4]. To this end, open source encourages collaboration among different stakeholders of the healthcare domain to build ever-changing and improving infrastructure technology [5]. This collaborative strategy has the potential to bring technology innovations into the healthcare domain much more quickly than independent development. Healthcare organizations need to understand what open source is and how it is significant to future health IT infrastructure innovations that will save money and help clinicians treat, monitor and advice patients more efficiently.
[1] Jayaraman, P. P., et al., (2020). Healthcare 4.0: A review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1350.
[2] Pandey, S. C. (2016). Data mining techniques for medical data: a review. In 2016 International Conference on Signal Processing, Communication, Power and Embedded System, pp. 972-982.
[3] Brandão, A., Pereira, E., Esteves, M., Portela, F., Santos, M. F., Abelha, A., & Machado, J. (2016). A benchmarking analysis of open-source business intelligence tools in healthcare environments. Information, 7(4), 57.
[4] Aminpour F, Sadoughi F, Ahmadi M. Towards the Application of Open Source Software in Developing National Electronic Health Record-Narrative Review Article. Iran J Public Health. 2013 Dec;42(12):1333-9. PMID: 26060634; PMCID: PMC4441929.
[5] Richterich, A. (2020). When open source design is vital: critical making of DIY healthcare equipment during the COVID-19 pandemic. Health Sociology Review, 29(2), 158-167.