SCENARIOS IN PHARMACEUTICALS AND HEALTH

6 studies available

Knowledge Web Partners: UPM
Business Partners: Robotiker
Challenge:
To provide an unified point of access to different biological data repositories accessible through the Internet, corporate databases, results of experiments, health cards, medical literature sites and so on.
Solution:
Application of semantic technologies to solve the inherent features of the biology field: huge quantity of dispersed, distributed and autonomous data with great difficulties to be integrated due to differences in terminology, syntax and semantics.
Why a semantic solution:
Ontologies describe the vocabulary of the data stored at each repository. Annotations describe the data and link it with a corresponding ontology. Ontology merging and mapping techniques allow integration of repositories in a consistent and unified way.
Key Business Benefits:
Aid to the researchers in the biological field, providing a unique point of access to biological data. For example, when a researcher wants to compare the results of an experiment with the genome annotation database.

Business Case Research Challenges

Knowledge Web Partners: VUB Star Lab
Business Partners: L&C Global
Challenge:
Data in a healthcare information system is dispersed and heterogeneous in a setting where speed of access and common presentation are important
Solution:
Integration and subsequent mediation of medical databases at the semantic level
Why a semantic solution:
Solutions focused on integrating databases tend to ignore the underlying meaning of the data and its structure so that an intelligent consolidation and presentation of data is not possible
Key Business Benefits:
Speed of access to the required data is vitally important in a healthcare setting as well as a common view on the data by different users

Business Case

Knowledge Web Partners: FU Berlin
Challenge:
A large health insurance company combines its data in a data warehouses to ensure data integration and consistency
Solution:
Introduce a common terminology for healthcare data and wrap all legacy data in this terminology
Why a semantic solution:
The provision of a common terminology allows for a data integration and consistency checking solution, in which a computer system can determine the relationship between data items and identify contradictions
Key Business Benefits:
There is a cost in time and money involved in a hard-coding of system and data integration, as well as costs to business where integration or consistency is not being handled (i.e. the costs of not having access to data, or having inconsistent data)

Business Case

Knowledge Web Partners: Cognium System
Challenge:
Biomedical research notes and raw data are difficult to share because they are not understandable to others unless they are well structured.
Solution:
A flexible, intuitive, and simple to use system for entering notes in a structured way (semantic markup) and then benefiting from the structure for sharing and managing the information.
Why a semantic solution:
Research information needs to be recorded in both structured and flexible manner, with complex interlinking. Semantic tagging paradigm corresponds best to all three of these requirements.
Key Business Benefits:
Decreased costs, faster work cycle, as well as higher quality of collaboration, project management, and communication.
Business Partners: Institut Pasteur, NEPOMUK consortium (EC funded).

Business Case

Knowledge Web Partners: FU Berlin
Challenge:
Experiences and lessons learned in the project "A Semantic Web for Pathology"
Solution:
Project using domain ontologies and ontology-driven natural language processing to support a content-based retrieval of text and image-based medical information. Building ontology based applications and using existing ontologies in new applications context.
Why a semantic solution:
In trying to develop the target application ontology we investigated the potential of reusing the huge amount of domain knowledge already available in ontology - like form in the medical domain as an input for the domain conceptualization the reuse of the huge amount of domain knowledge already available in ontology - like form in the medical domain
Key Business Benefits:
The system is able to answer content-related queries on image-based data (such as “images where the tissue sample presents certain morphological characteristics”), but also to compare textual medical reports or support differential diagnostics tasks (i.e. retrieve reports with a similar appearance, but alternative diagnostics).
Business Partners:

Business Case

Knowledge Web Partners: Agence de biomedecine
Challenge:
Better patient care, better understanding of diseases and sound decision making in public health require accessing large amounts of data distributed in heterogeneous sources
Solution:
A system, which allows semantic integration of the data A query engine based on a shared ontology Reformultation queries over the sources
Why a semantic solution:
Local centers use different schema and different terminologies for encoding the data. The current approach is to store the data in a common repository, asking the centers to enter their data in a standardized way through a Web server. Since data are not automatically integrated from the already existing information systems, clinicians have to register patient's data several times locally, regionally, nationally and in various forms
Key Business Benefits:
Prevent duplication of work for clinicians Assure quality and consistency of data Easier evolution and maintenance
Business Partners:
University Rennes 2

Business Case