15,144 research outputs found
Report on the EHCR (Deliverable 26.2)
This deliverable is the second for Workpackage 26. The first, submitted after
Month 12, summarised the areas of research that the partners had identified as
being relevant to the semantic indexing of the EHR. This second one reports
progress on the key threads of work identified by the partners during the project to
contribute towards semantically interoperable and processable EHRs.
This report provides a set of short summaries on key topics that have emerged as
important, and to which the partners are able to make strong contributions. Some of
these are also being extended via two new EU Framework 6 proposals that include
WP26 partners: this is also a measure of the success of this Network of Excellence
An Approach for Managing Access to Personal Information Using Ontology-Based Chains
The importance of electronic healthcare has caused numerous
changes in both substantive and procedural aspects of healthcare
processes. These changes have produced new challenges to patient
privacy and information secrecy. Traditional privacy policies cannot
respond to rapidly increased privacy needs of patients in electronic
healthcare. Technically enforceable privacy policies are needed in
order to protect patient privacy in modern healthcare with its cross
organisational information sharing and decision making.
This thesis proposes a personal information flow model that specifies
a limited number of acts on this type of information. Ontology
classified Chains of these acts can be used instead of the
"intended/business purposes" used in privacy access control to
seamlessly imbuing current healthcare applications and their
supporting infrastructure with security and privacy functionality. In
this thesis, we first introduce an integrated basic architecture, design
principles, and implementation techniques for privacy-preserving
data mining systems. We then discuss the key methods of privacypreserving
data mining systems which include four main methods:
Role based access control (RBAC), Hippocratic database, Chain
method and eXtensible Access Control Markup Language (XACML).
We found out that the traditional methods suffer from two main
problems: complexity of privacy policy design and the lack of context
flexibility that is needed while working in critical situations such as the
one we find in hospitals. We present and compare strategies for
realising these methods. Theoretical analysis and experimental
evaluation show that our new method can generate accurate data
mining models and safe data access management while protecting
the privacy of the data being mined. The experiments followed
comparative kind of experiments, to show the ease of the design first
and then follow real scenarios to show the context flexibility in saving
personal information privacy of our investigated method
Mining health knowledge graph for health risk prediction
Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patientsâ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patientsâ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health
The Requirements for Ontologies in Medical Data Integration: A Case Study
Evidence-based medicine is critically dependent on three sources of
information: a medical knowledge base, the patients medical record and
knowledge of available resources, including where appropriate, clinical
protocols. Patient data is often scattered in a variety of databases and may,
in a distributed model, be held across several disparate repositories.
Consequently addressing the needs of an evidence-based medicine community
presents issues of biomedical data integration, clinical interpretation and
knowledge management. This paper outlines how the Health-e-Child project has
approached the challenge of requirements specification for (bio-) medical data
integration, from the level of cellular data, through disease to that of
patient and population. The approach is illuminated through the requirements
elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three
diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database
Engineering & Applications Symposium (Ideas2007). Banff, Canada September
200
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
- âŚ