Ontologies Based Databases And Information Systems ((FULL))
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This paper describes a method for using Semantic Web technologies for sharing knowledge in healthcare. It combines deductive databases and ontologies, so that it is possible to extract knowledge that has not been explicitly declared within the database. A representation of the UMLS (Unified Medical Language System) Semantic Network and Metathesaurus was created using the RDF standard, in order to represent the basic medical ontology. The inference over the knowledge base is done by the TRI-DEDALO System, a deductive data-base created to query and update RDF based knowledge sources as well as conventional relational databases. Finally, an ontology was created for the Brazilian National Health Card data interchange format, a standard to capture and transmit health encounter information throughout the country. This paper demonstrates how this approach can be used to integrate heterogeneous information and to answer complex queries in a real world environment.
Abstract:Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.Keywords: formal ontology; information system; conceptualisation; software development; software design; semantic web; UML; OWL
There is a growing need to define a semantic mapping from a database schema to an ontology. Such a mapping is an integral part of the data integration systems that use an ontology as a unified global view. However, both ontologies and database schemas evolve over time in order to accommodate updated information needs. Once the ontology and the database schema associated with a semantic mapping evolved, it is necessary and important to maintain the validity of the semantic mapping to reflect the new semantics in the ontology and the schema. In this paper, we propose a formulation of the mapping maintenance problem and outline a possible solution using illustrative examples. The main points of this paper are: (1) to differentiate the semantic mapping maintenance problem from the schema mapping adaptation problem which only adapts mappings when schemas change; (2) to develop an approach for specifying the validity of a semantic mapping in terms of two-way legal instances translation between two models; (3) to explore the approach of using simple correspondences to capture changes to ontologies/schemas; and (4) to sketch a solution using examples.
Clinical decision support systems (CDSS) have been shown to reduce medication errors. However, they are underused because of different challenges. One approach to improve CDSS is to use ontologies instead of relational databases. The primary aim was to design and develop OntoPharma, an ontology based CDSS to reduce medication prescribing errors. Secondary aim was to implement OntoPharma in a hospital setting.
OntoPharma is an ontology based CDSS implemented in clinical practice which generates alerts when a prescribing medication error is identified. To gain user acceptance OntoPharma has been designed and developed by a multidisciplinary team. Compared to CDSS based on relational databases, OntoPharma represents medication knowledge in a more intuitive, extensible and maintainable manner.
Approaches involving information systems, such as computerised physician order entry (CPOE)  combined with clinical decision support systems (CDSS)  have been shown to reduce drug prescription errors.
CDSS link patient data with a knowledge base to generate information that help clinician make decisions . Relational databases are in most cases the system of choice when it comes to designing a CDSS. The potential of CDSS to reduce medication errors is clear. However, they are underused. There is growing literature about why clinicians fail to utilize CDSS suggestions . Lack of interoperability or alert fatigue explain high alert override rates [14,15,16,17]. Another challenge is the maintenance of the knowledge base up to date with the literature-based and practice-based evidence .
An ontology is an explicit conceptualization of the entities of a domain. It includes machine-interpretable definitions of concepts in the domain and relations among them [20, 21]. Since ontologies define the terms used to describe and represent an area of knowledge, they are used in many applications to facilitate data annotation, information retrieval or aid in education [22, 23]. Ontologies have the potential to support the development of CDSS in a manner that enhances reusability of data and knowledge. There are already existing ontology based CDSS representing a wide range of medical domains . However, only a few are addressed to medication management and usually, they are restricted to a specific disease or specialty.
Nomenclator for Prescription contains structured data in xml format. Contents from other resources are semi-structured data. Prior to modelling drug-related knowledge through ontologies, we processed all the information in a relational database to clean the data, detect redundancies and detect relationships between different concepts.
The design, development and maintenance of the ontologies have been driven by Medical Informatics specialists and Clinical Pharmacists. The information was represented in the Web Ontology Language (OWL) . For encoding the OWL ontologies, we used the Protégé 3.5 editor tool .
The integration between the CPOE system and the ontologies was performed through a REST API. A REST API call is published (in JSON format) each time a clinician adds a new medication in the CPOE, modify an existing one or request on demand CDSS information.
In addition, semantic approach and the use of OWL enable a convenient infrastructure for reusing. By contrast, databases are designed mainly to meet the requirements of a particular application. This makes it hard to reuse a database when requirements change, resulting in higher maintenance costs [31, 32]. Despite ontologies can readily be reused, OntoPharma was developed from scratch because none of the existing ontologies met our needs.
Because of these features previously discussed, in the last decade there is an increasing interest in the use of ontologies based CDSS . Some of them are addressed to medication management. However, they are focused on a specific subspecialty such as the management of chronic disease , cancer , antibiotic prescriptions , or diabetes , among others. In any case, it is not easy to make comparisons between ontologies. Although there are some guides about how to create ontologies, there is no one correct way to model a domain [38, 39]. The best solution depends on the final application and the extensions anticipated. For this reason, specialists in Medical Informatics have guided our ontologies design choices considering the clinician`s understanding and view of the medication domain. In addition, end users participation all along the design and development of OntoPharma ensures not interfere with their workflow and gain user acceptance [40, 41].
Ontologies enable operate on a higher level of abstraction so medication knowledge is represented in a more intuitive, extensible and maintainable manner in comparison with the initial dataset. For example, maximum daily dose source dataset contains various entries identified by the National Drug Code. In many cases, National Drug Codes are clinically equivalent pharmaceutical products with the same strength, dose form and the same routes of administration. We have defined maximum daily dose considering the ingredient and the route of administration. Thus, we have represented the same information using 562 individuals instead of 1013 entries. Similarly, we have defined drug-drug interactions considering ATC instead of National Drug Codes, so we have needed 2242 individuals instead of 3229 entries. We have also implemented the class "drug_route" which comprises all possible routes of administration. If, for example, the maximum dose of a drug is the same for all routes of administration, we only need one entry. In contrast, in traditional databases, an entry is needed for each route.To describe medication with a high level of detail, Drugs considers since the ingredient until the commercially produced packaged product. More than fifty properties defined at Drugs have been needed to represent all the technical data of medicinal products.
Another limitation of our study is to keep up with evidence. To date, we incorporate information from different sources including regulatory agencies and local practice-based evidence. However, evidence review is an extremely demanding and time-consuming process and often not easy to come by. Automatic update from resources for evidence-based medicine is an active area of inquiry . We consider that is a critical priority that has not been resolve. 2b1af7f3a8