Multi-Knowledge: Collaborative Environments for the Extraction of New Knowledge from Heterogenous Medical Data Sources

ICCS 2007 ◽  
2007 ◽  
pp. 69-77
Author(s):  
Michele Amoretti ◽  
Diego Ardigò ◽  
Franco Mercalli
Keyword(s):  
Medical Data ◽  
Data Sources ◽  
Collaborative Environments ◽  
New Knowledge

scholarly journals A Fog Computing-Based Architecture for Medical Records Management

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Cícero A. Silva ◽  
Gibeon S. Aquino ◽  
Sávio R. M. Melo ◽  
Dannylo J. B. Egídio
Keyword(s):  
Medical Records ◽  
Fog Computing ◽  
Medical Data ◽  
Data Sources ◽  
Medical Applications ◽  
Records Management ◽  
Medical Field ◽  
Computing Paradigm ◽  
And Storage

The aging of the world’s population and the growth in the number of people with chronic diseases have increased expenses with medical care. Thus, the use of technological solutions has been widely adopted in the medical field to improve the patients’ health. In this context, approaches based on Cloud Computing have been used to store and process the information generated in these solutions. However, using Cloud can create delays that are intolerable for medical applications. Thus, the Fog Computing paradigm emerged as an alternative to overcome this problem, bringing computation and storage closer to the data sources. However, managing medical data stored in Fog is still a challenge. Moreover, characteristics of availability, performance, interoperability, and privacy need to be considered in approaches that aim to explore this problem. So, this article shows a software architecture based on Fog Computing and designed to facilitate the management of medical records. This architecture uses Blockchain concepts to provide the necessary privacy features and to allow Fog Nodes to carry out the authorization process in a distributed way. Finally, this paper describes a case study that evaluates the performance, privacy, and interoperability requirements of the proposed architecture in a home-centered healthcare scenario.

PDA Usability for Telemedicine Support

2006 ◽  
pp. 457-462
Author(s):  
Shirley Ann Becker
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Healthcare Providers ◽  
Medical Data ◽  
Information And Communications Technology ◽  
Multimedia Data ◽  
Data Sources ◽  
Communications Technology ◽  
Healthcare Facilities ◽  
The Past

Telemedicine is broadly defined as the use of information and communications technology to provide medical information and services (Perednia & Allen, 1995). Telemedicine offers an unprecedented means of bringing healthcare to anyone regardless of geographic remoteness. It promotes the use of ICT for healthcare when physical distance separates the provider from the patient (Institute of Medicine, 1996). In addition, it provides for real-time feedback, thus eliminating the waiting time associated with a traditional healthcare visit. Telemedicine has been pursued for over three decades as researchers, healthcare providers, and clinicians search for a way to reach patients living in remote and isolated areas (Norris, 2001). Early implementation of telemedicine made use of the telephone in order for healthcare providers and patients to interact. Over time, fax machines were introduced along with interactive multimedia, thus supporting teleconferencing among participants. Unfortunately, many of the early telemedicine projects did not survive because of high costs and insurmountable barriers associated with the use of technology. Telemedicine has been resurrected during the last decade as a means to help rural healthcare facilities. Advances in information and communications technology have initiated partnerships between rural healthcare facilities and larger ones. The Internet in particular has changed the way in which medical consultations can be provided (Coiera, 1997). Personal computers (PCs) and supporting peripherals, acting as clients, can be linked to medical databases residing virtually in any geographic space. Multimedia data types, video, audio, text, imaging, and graphics promote the rapid diagnosis and treatment of casualties and diseases. Innovations in ICT offer unprecedented healthcare opportunities in remote regions throughout the world. Mobile devices using wireless connectivity are growing in popularity as thin clients that can be linked to centralized or distributed medical-data sources. These devices provide for local data storage of medical data, which can be retrieved and sent back to a centralized source when Internet access becomes available. Those working in nomadic environments are connected to data sources that in the past were inaccessible due to a lack of telephone and cable lines. For the military, paramedics, social workers, and other healthcare providers in the field, ICT advances have removed technology barriers that made mobility difficult if not impossible. Personal digital assistants (PDAs)1 are mobile devices that continue to grow in popularity. PDAs are typically considered more usable for multimedia data than smaller wireless devices (e.g., cell phones) because of larger screens, fully functional keyboards, and operating systems that support many desktop features. Over the past several years, PDAs have become far less costly than personal-computing technology. They are portable, lightweight, and mobile when compared to desktop computers. Yet, they offer similar functionality scaled back to accommodate the differences in user-interface designs, data transmission speed, memory, processing power, data storage capacity, and battery life.

Data Sharing and Privacy in Pharmaceutical Studies

2021 ◽  
Vol 27 ◽  
Author(s):  
Rufan Chen ◽  
Yi Zhang ◽  
Zuochao Dou ◽  
Feng Chen ◽  
Kang Xie ◽  
...  
Keyword(s):  
Public Health ◽  
Information Exchange ◽  
Privacy Protection ◽  
Disease Burden ◽  
Adverse Drug Events ◽  
Health Information Exchange ◽  
Medical Data ◽  
Data Sources ◽  
Pharmacovigilance System ◽  
Potential Risks

Abstract: Adverse drug events have been a long-standing concern for the wide-ranged harms on public health, and the substantial disease burden. The key to diminish or eliminate the impacts is to build a comprehensive pharmacovigilance system. Application of the “big data” approach has been proved to assist the detection of adverse drug events by involving previously unavailable data sources and promoting health information exchange. Even though, challenges and potential risks still remain. The lack of effective privacy-preserving measures in the flow of medical data is the most important Accepted: one, where urgent actions are required to prevent the threats and therefore facilitate the construction of pharmacovigilance systems. Several privacy protection methods are reviewed in this article, which may be helpful to break the barrier.

scholarly journals Medical data exploration based on the heterogeneous data sources aggregation system

2019 ◽  
Author(s):  
Andrzej Opaliński ◽  
Krzysztof Regulski ◽  
Barbara Mrzygłód ◽  
Mirosław Głowacki ◽  
Aleksander Kania ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Medical Data ◽  
Data Exploration ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals Distributed Skin Lesion Analysis Across Decentralised Data Sources

2021 ◽  
Author(s):  
Yongli Mou ◽  
Sascha Welten ◽  
Mehrshad Jaberansary ◽  
Yeliz Ucer Yediel ◽  
Toralf Kirsten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Skin Lesion ◽  
Medical Data ◽  
Data Sources ◽  
Cancer Type ◽  
Local Data ◽  
Single Location ◽  
Lesion Analysis ◽  
Unlimited Access ◽  
Common Cancer Type

Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location and execute the analysis tasks on central servers. However, centralisation of medical data does not often comply with local data protection regulations due to its sensitive nature and the loss of sovereignty if data providers allow unlimited access to the data. The Personal Health Train (PHT) is a Distributed Analytics (DA) infrastructure bringing the algorithms to the data instead of vice versa. By following this paradigm shift, it proposes a solution for persistent privacy- related challenges. In this work, we present a feasibility study, which demonstrates the capability of the PHT to perform statistical analyses and Machine Learning on skin lesion data distributed among three Germany-wide data providers.

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