Alcuni abstract di articoli che trattano argomenti relativi all'eHealth

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Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures

Improving privacy in identity management systems for health care scenarios

Privacy is a very complex and subjective concept with different meaning to different people. The meaning depends on the context. Moreover, privacy is close to the user information and thus, present in any ubiquitous computing scenario. In the context of identity management (IdM), privacy is gaining more importance since IdM systems deal with services that requires sharing attributes belonging to users’ identity with different entities across domains. Consequently, privacy is a fundamental aspect to be addressed by IdM to protect the exchange of user attributes between services and identity providers across different networks and security domains in pervasive computing. However, problems such as the effective revocation consent, have not been fully addressed. Furthermore, privacy depends heavily on users and applications requiring some degree of flexibility. This paper analyzes the main current identity models, as well as the privacy support presented by the identity management frameworks. After the main limitations are identified, we propose a delegation protocol for the SAML standard in order to enhance the revocation consent within healthcare scenarios.Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madri

Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems

Semantic interoperability within the health care sector requires that patient data be fully available and shared without ambiguity across participating health facilities. Ongoing discussions to achieve interoperability within the health care industry continue to emphasize the need for healthcare facilities to successfully adopt and implement Electronic Health Record (EHR) systems. Reluctance by the healthcare industry to implement these EHRs for the purpose of achieving interoperability has led to the proposed research problem where it was determined that there is no existing single data standardization structure that can effectively share and interpret patient data within heterogeneous systems. \ \ The proposed research proposes a master data standardization and translation (MDST) model – XDataRDF -- which incorporates the use of the Resource Description Framework (RDF) that will allow for the seamless exchange of healthcare data among multiple facilities. Using RDF will allow multiple data models and vocabularies to be easily combined and interrelated within a single environment thereby reducing data definition ambiguity.

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

DHRS 2009 Proceedings of the Ninth Danish Human-Computer Interaction Research Symposium.

Since 2001 the annual Danish Human-Computer Interaction Research Symposium has been a platform for networking, and provided an opportunity to get an overview across the various parts of the Danish HCI research scene. This years symposium was held in Aarhus, Denmark on December 14, 200

Concealment and Discovery: The Role of Information Security in Biomedical Data Re-Use

This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in the material, social and regulatory landscapes of research. We consider the data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region; and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers from the University of Exeter, the London School of Hygiene and Tropical Medicine, the Met Office and Public Health England to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts; and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalised in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the re-usability of biomedical data, while encouraging exploratory analyses of secondary data sources