» Articles » PMID: 32210505

Generating and Knowledge Framework: Design and Open Specification

Overview
Journal Acta Inform Med
Specialty General Medicine
Date 2020 Mar 27
PMID 32210505
Citations 6
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: The Information Aggregation (IA) component manages streaming and batch data deriving from a multitude sources in a scalable, efficient and reliable way to create Holistic Health Records (HHRs).Within this context, the IA component combines a number of diverse data sources into a common format and stores information in an available form to be used for analytics, simulations and decision making.

Aim: The purpose of this paper is to provide an overview of the CrowdHEALTH project and the technical architecture of the CrowdHEALTH platform in order to put the aforementioned IA mechanism in context. This is followed by the design details and initial specifications of the first prototype of the IA component as well as its relationship with other components.

Methods: The micro-service approach can be used to perform information aggregation and to update HHRs in the CrowdHEALTH platform. Micro-services are a variant of the service-oriented architecture (SOA) where applications are structured as a collection of loosely coupled services with defined interfaces.

Results: Within the CrowdHEALTH architecture, the Information Aggregation component is situated between the Interoperability Layer and the CrowdHEALTH Datastore. The Information Aggregation component processes and aggregates interoperable data, before data aggregation in the HHRs and storage in the big datastore of CrowdHEALTH platform. The aggregation functions use big data management techniques and enhance the state of the art in specific areas such as the use of micro-services to perform synchronous aggregation operations on heterogeneous datasets.

Conclusions: Although an initial version of the IA component was presented, the specifications and implementation level details will be further updated during the project's course.

Citing Articles

Application of Modular Architectures in the Medical Domain - a Scoping Review.

Bathelt F, Lorenz S, Weidner J, Sedlmayr M, Reinecke I J Med Syst. 2025; 49(1):27.

PMID: 39964566 PMC: 11835905. DOI: 10.1007/s10916-025-02158-3.


Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies.

Moutselos K, Maglogiannis I Med Arch. 2020; 74(1):47-53.

PMID: 32317835 PMC: 7164729. DOI: 10.5455/medarh.2020.74.47-53.


Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

Minou J, Mantas J, Malamateniou F, Kaitelidou D Med Arch. 2020; 74(1):39-41.

PMID: 32317833 PMC: 7164736. DOI: 10.5455/medarh.2020.74.39-41.


Health in All Policy Making Utilizing Big Data.

Vassiliou A, Georgakopoulou C, Papageorgiou A, Georgakopoulos S, Goulas S, Paschalis T Acta Inform Med. 2020; 28(1):65-70.

PMID: 32210518 PMC: 7085317. DOI: 10.5455/aim.2020.28.65-70.


Modelling and Evaluation of Policies.

Moutselos K, Maglogiannis I, Kyriazis D, Granados A, Plagianakos V, Papageorgiu A Acta Inform Med. 2020; 28(1):58-64.

PMID: 32210517 PMC: 7085313. DOI: 10.5455/aim.2020.28.58-64.


References
1.
Mantas J . Future trends in Health Informatics--theoretical and practical. Stud Health Technol Inform. 2005; 109:114-27. View

2.
Mena L, Felix V, Ostos R, Gonzalez J, Cervantes A, Ochoa A . Mobile personal health system for ambulatory blood pressure monitoring. Comput Math Methods Med. 2013; 2013:598196. PMC: 3665224. DOI: 10.1155/2013/598196. View

3.
Chawla N, Davis D . Bringing big data to personalized healthcare: a patient-centered framework. J Gen Intern Med. 2013; 28 Suppl 3:S660-5. PMC: 3744281. DOI: 10.1007/s11606-013-2455-8. View

4.
Kyriazis D, Autexier S, Brondino I, Boniface M, Donat L, Engen V . CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health. Stud Health Technol Inform. 2017; 238:19-23. View