DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASES

Computers in Biology and Medicine, 2006

Ontology is no longer a mere research topic, but its relevance has been recognized in several practical fields. Current applications areas include natural language translation, e-commerce, geographic information systems, legal information systems and biology and medicine. It is the backbone of solid and effective applications in health care and can help to build more powerful and more interoperable medical information systems. The design and implementation of ontologies in medicine is mainly focused on the re-organization of medical terminologies. This is obviously a difficult task and requires a deep analysis of the structure and the concepts of such terminologies, in order to define domain ontologies able to provide both flexibility and consistency to medical information systems. The aim of this special issue of Computers in Biology and Medicine is to report the current evolution of research in biomedical ontologies, presenting both papers devoted to methodological issues and works with a more applicative emphasys.

International Journal of Scientific and Technological Research

Retrieving the information needed for right clinical decision and searching through a large amount of data are important challenges that health information systems encountered despite the advances in the information systems. In order to reuse these distributed data, the health information standards are developed to enable interoperable health information systems. As the standards are far from the necessary semantics, the data defined according to these standards are not been efficiently used by machines. Semantic Web technologies provide a common framework to access and process the information by machines with the support of ontologies. The heterogeneous and distributed nature of health data makes it a very suitable candidate to define health domain specific ontologies. In this work, a methodology for defining health ontologies, which should work as information base of a health information system as the next stage, is proposed to ensure interoperability and reuse between health information systems with the support of health information standards. Clinical Biochemistry Laboratory Ontology (CBLO) is given as a use case to model information of laboratory tests realized in clinical biochemistry laboratory that are a sub-domain of medical laboratory tests. In addition, a weighted evaluation for the proposed methodology is presented.

Computers, 2021

Noncommunicable chronic diseases (NCDs) affect a large part of the population. With the emergence of COVID-19, its most severe cases impact people with NCDs, increasing the mortality rate. For this reason, it is necessary to develop personalized solutions to support healthcare considering the specific characteristics of individuals. This paper proposes an ontology to represent the knowledge of educational assistance in NCDs. The purpose of ontology is to support educational practices and systems oriented towards preventing and monitoring these diseases. The ontology is implemented under Protégé 5.5.0 in Ontology Web Language (OWL) format, and defined competency questions, SWRL rules, and SPARQL queries. The current version of ontology includes 138 classes, 31 relations, 6 semantic rules, and 575 axioms. The ontology serves as a NCDs knowledge base and supports automatic reasoning. Evaluations performed through a demo dataset demonstrated the effectiveness of the ontology. SWRL rules...

Diabetes mellitus is a major cause of morbidity and mortality in humans. Early diagnosis is the first step toward the management of this condition. However, a diagnosis involves several variables, which makes it difficult to arrive at an accurate and timely diagnosis and to construct accurate personalized treatment plans. An electronic health record system requires an integrated decision support capability, and ontologies are rapidly becoming necessary for the design of efficient, reliable, extendable, reusable, and semantically intelligent knowledge bases. In this study, we take the first step in this direction, by designing an OWL2 diabetes diagnosis ontology (DDO). Protégé 5 software was used for the construction of the ontology. DDO is developed within the framework of the basic formal ontology and the ontology for general medical science to represent entities in the domain of diabetes, and it follows the design principles recommended by the Open Biomedical Ontology Foundry. Currently, DDO contains 6444 concepts, 48 properties, 13,551 annotations, and 27,127 axioms. DDO can serve as a diabetes knowledge base and supports automatic reasoning. It represents a major step toward the development of a new generation of patient‑centric decision support tools. DDO is available through BioPortal at: http://www.bioportal.bioontology.org/ontologies/DDO.

Ontology as a creative tool plays an essential role in acquisition and organization of knowledge and its representation in a special field. The important point is that ontology is usually applied in designing information systems and is useful for providing healthcare in design of health information systems by providing a comprehensive model of information and process needs. This comprehensive review study is based on existing resources and articles. Considering that a special methodology is used for creation and development of ontology in each domain, therefore, a methodology was presented for designing ontology of health information systems in the conducted studies and based on the general model by Pinto and Martin. The general model was changed in healthcare in order to support design and evaluation of information systems. This systematic approach includes the following three groups; stages, activity, and output. These stages include: specification, conceptualization, formalization, implementation, evaluation and maintenance. It should be noted that two main challenges are observed in designing ontology-based health information systems: designing useful and accurate comprehensive ontologies in healthcare systems, the second challenge is how ontologies should be coordinated with designing and evaluating health systems.Accepting a framework based on ontology provides a strong opportunity for integration and analysis of supervision system data and allows for executing rules based on evaluation in domain of health information systems.

This paper presents the design and implementation of a Health Ontology Generator (HOG) using a health database such as Microsoft Access or SQL Server. The development of the ontology generator involves building methods for creating and reading the ontology. This research performs both these tasks. In generating the ontology, database tables are treated as classes, fields as functional properties, and records as instances. The ontology generated can be read using third-party software such as Microsoft Word, Excel and Internet Explorer. HOG is implemented using C#.NET on the Windows platform.

The Differential diagnosis (ddx) is a systematic method to identify unknowns. This method, essentially a process of elimination, is used by taxonomists to identify living organisms and by physicians to diagnose the specific disease in a patient. One important point is the enormous amount of knowledge that doctors are required to possess, in order to be able to make a correct ddx. The main problem is that the number of diseases which exist worldwide is enormous, and it is beyond the scope of the human brain to remember them all. For this reason, in this paper we present MEDBOLI, an ontology-driven medical diagnosis system which applies the use of ontologies combined with logical inference and computation of probabilities to establish the probability of the diagnosis.

Revue d'Intelligence Artificielle

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

International Journal of Computer Applications, 2015

Human disease data is a cornerstone of biomedical research for diseases' classification and recommended treatments so; there is a significant need for a standardized representation of human diseases and an efficient algorithm for retrieving information from it. The Semantic Doctor Assistant (SDA) has been designed to help doctors to find proper information about a specific disease using semantic web technology rather than other simple keyword-based search. A preliminary usability study has been done to evaluate the system by measuring user's satisfaction through a statistical analysis of surveys. This study would measure the relevance of the information retrieved for each search query and how the system is important in the field of medicine and how it will help academic doctors in their research and non-academic doctors in their work.

Iranian Journal of Medical Informatics

Background and Aim: This article will discuss Semantic Web standards and ontologies in two areas: (1) the research and (2) healthcare. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems.Methods: This review article was performed based on a literature review and internet search through scientific databases such as PubMed, Scopus, Web of science and Google Scholar.Conclusion: Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information...