beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data
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.
Argyro Mavrogiorgou, Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos
Andreas Menychtas, Athanasios Kiourtis, Ilias Maglogiannis, Dimosthenis Kyriazis
Analyzing Collective Knowledge Towards Public Health Policy Making
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.
Spyridon Kleftakis, Konstantinos Mavrogiorgos,
Nikolaos Zafeiropoulos, Argyro Mavrogiorgou,
Athanasios Kiourtis, Ilias Maglogiannis, Dimosthenis Kyriazis
An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data
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...
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.
Argyro Mavrogiorgou, Athanasios Kiourtis, George Manias, Dimosthenis Kyriazis