An Ontology for Clinical Trial Data Integration

2000

Ontologies are being ever more commonly used in biomedical informatics and we provide a survey of some of these uses, and of the relations between ontologies and other terminology resources. In order for ontologies to become truly useful, two objectives must be met. First, ways must be found for the transparent evaluation of ontologies. Second, existing ontologies need to be

JMIR medical informatics, 2023

Background: To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. Objective: The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. Methods: First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. Results: Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence,"

Motivated by our experiences of representing clinical information using OWL DL, which often resulted in highly complex expressions, we propose the use of ontology content patterns to facilitate this task. They are based on a set of formal ontologies, constrained by the concepts and relations of a top-level one, which reduces arbitrariness in ontology design. We propose their application to information encoded by electronic health records specifications and ontology-based terminologies, in order to provide semantic interoperability across heterogeneously represented data, and to guide the creation of clinical models and detect semantic inconsistencies across them. We provide examples of their application to achieve the above mentioned tasks and discuss the limitations and further research issues.

2008

Ontologies are being ever more commonly used in biomedical informatics and we provide a survey of some of these uses, and of the relations between ontologies and other terminology resources. In order for ontologies to become truly useful, two objectives must be met. First, ways must be found for the transparent evaluation of ontologies. Second, existing ontologies need to be harmonised. We argue that one key foundation for both ontology evaluation and harmonisation is the adoption of a realist paradigm in ontology development.

arXiv preprint arXiv:1210.4405, 2012

Abstract: There is a growing need to semantically process and integrate clinical data from different sources for Clinical Data Management and Clinical Decision Support in the healthcare IT industry. In the clinical practice domain, the semantic gap between clinical information systems and domain ontologies is quite often difficult to bridge in one step. In this paper, we report our experience in using a two-step formalization approach to formalize clinical data, ie from database schemas to local formalisms and from local formalisms to ...

IEEE Transactions on Information Technology in Biomedicine, 1998

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.

Studies in health technology and informatics, 2008

Ontologies are being ever more commonly used in biomedical informatics. The paper provides a survey of some of these uses, and of the relations between ontologies and other terminology resources. In order for ontologies to become truly useful, two objectives must be met. First, ways must be found for the transparent evaluation of ontologies. Second, existing ontologies need to be harmonized. The authors argue that one key foundation for both ontology evaluation and harmonization is the adoption of a realist paradigm in ontology development. For science-based ontologies of the sort which concern us in the eHealth arena, it is reality that provides the common benchmark against which ontologies can be evaluated and aligned within larger frameworks. Given the current multitude of ontologies in the biomedical domain the need for harmonization is becoming ever more urgent. An example of such harmonization within the ACGT project is described, which draws on ontology-based computing as a b...

2014 IEEE International Conference on Bioinformatics and Bioengineering, 2014

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informaticssupported applications in clinical care and research both within a single healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carry out data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable. I.

PloS one, 2015

Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can th...