Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Semantic web technologies provide a standardised mechanism for describing and accessing the services of underlying infrastructure. These technologies facilitate the inclusion of the quality of network services in the control loop of high level applications and allow applications to tune the system level performance with additional quality dimensions. However, the descriptions of a large infrastructure are often composed and maintained by different parties and can have different levels of details because of the administration policies. These facts make the development of high level applications unnecessarily difficult. We present a preprocessing framework to hide these difficulties from high level application developers by transforming, integrating, and filtering raw descriptions of the infrastructure into proper information content that these applications need.

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“…In this context, detecting inconsistencies between data sources and mapping data schemas are two challenging issues. In [11], Anguita et. al.…”

Section: A Related Workmentioning

confidence: 97%

Resource Discovery in Large Scale Network Infrastructure

Zhao

Taal

Grosso

et al. 2011

2011 IEEE Sixth International Conference on Networking, Architecture, and Storage

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“…They represented a "light-weight" approach to interconnect existent data sources, because of legal and regulatory restrictions. Another study about integration is given in [23]. They developed various methods and tools for database integration, because they mention that different biological and clinical research projects are based on collaborative efforts among international organizations.…”

Section: Integration Of Medical Databasesmentioning

confidence: 99%

Data Mining on Distributed Medical Databases: Recent Trends and Future Directions

Atilgan

Dogan

2009

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. As computerization in healthcare services increase, the amount of available digital data is growing at an unprecedented rate and as a result healthcare organizations are much more able to store data than to extract knowledge from it. Today the major challenge is to transform these data into useful information and knowledge. It is important for healthcare organizations to use stored data to improve quality while reducing cost. This paper first investigates the data mining applications on centralized medical databases, and how they are used for diagnostic and population health, then introduces distributed databases. The integration needs and issues of distributed medical databases are described. Finally the paper focuses on data mining studies on distributed medical databases.

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“…Obligations to improve efficiency in healthcare facilities, have stimulated studies for the integration of medical data sources that are naturally distributed [18]. Several database integration methods have been introduced in [43] for around 900 databases. The distinction between biological and clinical data has been taken into account during the process.…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

et al. 2021

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Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralized EHRs, such as data size, privacy and data ownership consideration. In this paper, we propose a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules. These medical data resources cannot be moved to other network sites. Therefore, the desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In the proposed methodology, each distributed data source is represented by an agent. The global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network, this will preserve the privacy and the security of the local data. The proposed association rule mining methodology will be used for heart disease prediction using real heart disease data. These real data exist at different clinics and cannot be moved to a central site. The proposed model protects the patient data privacy and achieves the same results as if the data are moved and joined at a central site. We also validate the extracted association rules from all the data providers using an independent test datasets.INDEX TERMS Association rules, medical distributed databases, electronic health care records, agents, data privacy.

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Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Abstract: Abstract

Cited by 3 publications

References 13 publications

Resource Discovery in Large Scale Network Infrastructure

Resource Discovery in Large Scale Network Infrastructure

Data Mining on Distributed Medical Databases: Recent Trends and Future Directions

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

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