Δημοσιεύσεις

beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data

14/07/2021

Περίληψη

The era of big data is surrounded by plenty of challenges, concerning aspects related to data quality, data management, and data analysis. Plenty of these challenges are met in several domains, such... as the healthcare domain, where the corresponding healthcare platforms not only have to deal with managing and/or analyzing a tremendous quantity of health data, but also have to accomplish these actions in the most efficient and secure way possible. Towards this direction, medical institutions are paying attention to the replacement of traditional approaches such as the Monolithic and Service Oriented Architecture (SOA), which deal with many difficulties for handling the increasing amount of healthcare data. This paper presents a platform for overcoming these issues, by adopting the Microservice Architecture (MSA), being able to efficiently manage and analyze these vast amounts of data. More specifically, the proposed platform, namely beHEALTHIER, offers the ability to construct health policies out of data of collective knowledge, by utilizing a newly proposed kind of electronic health records (i.e., eXtended Health Records (XHRs)) and their corresponding networks, through the efficient analysis and management of ingested healthcare data. In order to achieve that, beHEALTHIER is architected based upon four (4) discrete and interacting pillars, namely the Data, the Information, the Knowledge and the Actions pillars. Since the proposed platform is based on MSA, it fully utilizes MSA's benefits, achieving fast response times and efficient mechanisms for healthcare data collection, processing, and analysis.

Συγγραφείς

Argyro Mavrogiorgou, Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos Andreas Menychtas, Athanasios Kiourtis, Ilias Maglogiannis, Dimosthenis Kyriazis

Περισσότερα

Analyzing Collective Knowledge Towards Public Health Policy Making

07/07/2021

Περίληψη

Nowadays there exists a plethora of diverse data sources producing tons of healthcare data, augmenting the size of data that finally is stored both in Electronic Health Records (EHRs) and in Personal Health Records (PHRs).... Thus, the great challenge that emerges is not only to gather all this data in an efficient and effective manner, but also to extract knowledge out of it. The latter is the key factor that enables healthcare professionals to take serious clinical decisions both on individual and on collective level, finally forming representative public health policies. Towards this direction, the current paper proposes a system that supports a new paradigm of EHRs, the eXtended Health Records (XHRs), which include the majority of the health determinants. XHRs are then transformed into XHRs Networks that capture the clinical, social and human context of diverse population segmentations, producing the corresponding collective knowledge. By exploiting this knowledge, the proposed system is finally able to create multi-modal policies, addressing various facts and evolving risks that arise from diverse population segmentations.

Συγγραφείς

Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos, Argyro Mavrogiorgou, Athanasios Kiourtis, Ilias Maglogiannis, Dimosthenis Kyriazis

Περισσότερα

An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data

01/07/2021

Περίληψη

Nowadays organizations are surrounded with enormous amounts of data, losing all the important information that resides in it. Knowledge Discovery in Databases (KDD) can aid organizations to transform this data into valuable... information, by extracting complex patterns and relationships from it. To achieve that, various KDD techniques and tools have been proposed, resulting into impressive outcomes in various domains, especially in healthcare. Due to the huge amount of data available within the healthcare systems, data mining is extremely important for the healthcare sector. However, what is of major importance as well, is the way through which the data is collected, preprocessed and integrated with each other, considering its heterogeneous and diverse nature and format. To address all these challenges, this paper proposes a generalized KDD approach, which in essence constitutes a supplement of all the existing approaches that study and analyse the data mining part of the KDD process. This approach primarily concentrates on the phases of the selection, the preprocessing, as well as the transformation of the collected healthcare data, which are considered to be of great importance for its successful mining, analysis, and interpretation. The prototype of the proposed approach provides an example of the developed mechanism, explaining in deep detail its phases, verifying its possible wide applicability and adoption in various healthcare scenarios.

Συγγραφείς

Argyro Mavrogiorgou, Athanasios Kiourtis, George Manias, Dimosthenis Kyriazis

Περισσότερα