Structuring, reuse and analysis of electronic dental data using the Oral Health and Disease Ontology.

BACKGROUND: A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines' EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework. RESULTS: The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice's EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance - a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies. CONCLUSIONS: This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

The Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

An ontology-based method for secondary use of electronic dental record data.

A key question for healthcare is how to operationalize the vision of the Learning Healthcare System, in which electronic health record data become a continuous information source for quality assurance and research. This project presents an initial, ontology-based, method for secondary use of electronic dental record (EDR) data. We defined a set of dental clinical research questions; constructed the Oral Health and Disease Ontology (OHD); analyzed data from a commercial EDR database; and created a knowledge base, with the OHD used to represent clinical data about 4,500 patients from a single dental practice. Currently, the OHD includes 213 classes and reuses 1,658 classes from other ontologies. We have developed an initial set of SPARQL queries to allow extraction of data about patients, teeth, surfaces, restorations and findings. Further work will establish a complete, open and reproducible workflow for extracting and aggregating data from a variety of EDRs for research and quality assurance.

From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation.

Translational science, today, involves multidisciplinary teams of scientists rather than single scientists. Teams facilitate biologically meaningful and clinically consequential breakthroughs. There are a myriad of sources of data about investigators, physicians, research resources, clinical encounters, and expertise to promote team interaction; however, much of this information is not connected and is left siloed. Large amounts of data have been published as Linked Data (LD), but there still remains a significant gap in the representation and connection of research resources and clinical expertise data. The CTSAconnect project addresses the problem of fragmentation and incompatible coding of information by creating a Semantic Framework that facilitates the production and consumption of LD about biomedical research resources, clinical activities, as well as investigator and physician expertise.

Research resources: curating the new eagle-i discovery system.

Development of biocuration processes and guidelines for new data types or projects is a challenging task. Each project finds its way toward defining annotation standards and ensuring data consistency with varying degrees of planning and different tools to support and/or report on consistency. Further, this process may be data type specific even within the context of a single project. This article describes our experiences with eagle-i, a 2-year pilot project to develop a federated network of data repositories in which unpublished, unshared or otherwise 'invisible' scientific resources could be inventoried and made accessible to the scientific community. During the course of eagle-i development, the main challenges we experienced related to the difficulty of collecting and curating data while the system and the data model were simultaneously built, and a deficiency and diversity of data management strategies in the laboratories from which the source data was obtained. We discuss our approach to biocuration and the importance of improving information management strategies to the research process, specifically with regard to the inventorying and usage of research resources. Finally, we highlight the commonalities and differences between eagle-i and similar efforts with the hope that our lessons learned will assist other biocuration endeavors. DATABASE URL: www.eagle-i.net.

Uberon, an integrative multi-species anatomy ontology.

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org.