A Neuro-ontology for the neurological examination.

BACKGROUND: The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as machine-readable codes based on UMLS Metathesaurus concepts. METHODS: We created a target ontology for capturing the neurological examination using 1100 concepts from the UMLS Metathesaurus. We created a dataset of 2386 test-phrases based on 419 published neurological cases. We then mapped the test-phrases to the target ontology. RESULTS: We were able to map all of the 2386 test-phrases to 601 unique UMLS concepts. A neurological examination ontology with 1100 concepts has sufficient breadth and depth of coverage to encode all of the neurologic concepts derived from the 419 test cases. Using only pre-coordinated concepts, component ontologies of the UMLS, such as HPO, SNOMED CT, and OMIM, do not have adequate depth and breadth of coverage to encode the complexity of the neurological examination. CONCLUSION: An ontology based on a subset of UMLS has sufficient breadth and depth of coverage to convert deficits from the neurological examination into machine-readable codes using pre-coordinated concepts. The use of a small subset of UMLS concepts for a neurological examination ontology offers the advantage of improved manageability as well as the opportunity to curate the hierarchy and subsumption relationships.

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. METHODS: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. RESULTS: Ontologies for "food and nutrition" (n = 37), "disease and specific population" (n = 100), "data description" (n = 21), "research description" (n = 35), and "supplementary (meta) data description" (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. CONCLUSION: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

A flexible ontology for inference of emergent whole cell function from relationships between subcellular processes.

Whole cell responses arise from coordinated interactions between diverse human gene products functioning within various pathways underlying sub-cellular processes (SCP). Lower level SCPs interact to form higher level SCPs, often in a context specific manner to give rise to whole cell function. We sought to determine if capturing such relationships enables us to describe the emergence of whole cell functions from interacting SCPs. We developed the Molecular Biology of the Cell Ontology based on standard cell biology and biochemistry textbooks and review articles. Currently, our ontology contains 5,384 genes, 753 SCPs and 19,180 expertly curated gene-SCP associations. Our algorithm to populate the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to the continuously growing cell biological knowledge. Since whole cell responses most often arise from the coordinated activity of multiple SCPs, we developed a dynamic enrichment algorithm that flexibly predicts SCP-SCP relationships beyond the current taxonomy. This algorithm enables us to identify interactions between SCPs as a basis for higher order function in a context dependent manner, allowing us to provide a detailed description of how SCPs together can give rise to whole cell functions. We conclude that this ontology can, from omics data sets, enable the development of detailed SCP networks for predictive modeling of emergent whole cell functions.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology.

INTRODUCTION: We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. MATERIAL AND METHODS: The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. RESULTS: The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05]. DISCUSSION: The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

Ontology-Oriented Programming for Biomedical Informatics.

Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites. Two main approaches have been proposed for accessing ontologies in a computer program: traditional API (Application Programming Interface) and ontology-oriented programming, either static or dynamic. In this paper, we will review these approaches and discuss their appropriateness for biomedical ontologies. We will also present an experience feedback about the integration of an ontology in a computer software during the VIIIP research project. Finally, we will present OwlReady, the solution we developed.

Ontological Foundations for Tracking Data Quality through the Internet of Things.

Amongst the positive outcomes expected from the Internet of Things for Health are longitudinal patient records that are more complete and less erroneous by complementing manual data entry with automatic data feeds from sensors. Unfortunately, devices are fallible too. Quality control procedures such as inspection, testing and maintenance can prevent devices from producing errors. The additional approach envisioned here is to establish constant data quality monitoring through analytics procedures on patient data that exploit not only the ontological principles ascribed to patients and their bodily features, but also to observation and measurement processes in which devices and patients participate, including the, perhaps erroneous, representations that are generated. Using existing realism-based ontologies, we propose a set of categories that analytics procedures should be able to reason with and highlight the importance of unique identification of not only patients, caregivers and devices, but of everything involved in those measurements. This approach supports the thesis that the majority of what tends to be viewed as 'metadata' are actually data about first-order entities.

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.

Harmonizing SNOMED CT with BioTopLite: An Exercise in Principled Ontology Alignment.

The integration of heterogeneous ontologies is often hampered by different upper level categories and relations. We report on an on-going effort to align clinical terminology/ontology SNOMED CT with the formal upper-level ontology BioTopLite. This alignment introduces several constraints at the OWL-DL level. The mapping was done manually by analysing formal and textual definitions. Descriptive logic classifiers interactively checked mapping steps, using small modules for increasing performance. We present an effective workflow, using modules of several scales. However, only part of the classes and relations could easily be mapped. The implications for future evolution of SNOMED CT are discussed. It seems generally feasible to use a highly constrained upper-level ontology as an upper level for the benefit of future SNOMED CT versions that are more interoperable with other biomedical ontologies.

Ontology-Driven Semantic Search for Brazilian Portuguese Clinical Notes.

The emerging penetration of Health IT in Latin America (especially in Brazil) has exacerbated the ever-increasing amount of Electronic Health Record (EHR) clinical free text documents.This imposes a workflow efficiency challenge on clinicians who need to synthesize such documents during the typically time-constrained patient care. We propose an ontology-driven semantic search framework that effectively supports clinicians' information synthesis at the point of care.

Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

There remain significant difficulties selecting probable candidate drugs from existing databases. We describe an ontology-oriented approach to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources. We also report a case study in which we attempted to explore candidate drugs effective for bipolar disorder and epilepsy. We constructed an ontology incorporating knowledge between the two diseases and performed semantic reasoning tasks with the ontology. The results suggested 48 candidate drugs that hold promise for further breakthrough. The evaluation demonstrated the validity our approach. Our approach prioritizes the candidate drugs that have potential associations among genes, phenotypes and symptoms, and thus facilitates the data integration and drug repurposing in psychiatric disorders.

[Big data, medical language and biomedical terminology systems]. / Big Data, medizinische Sprache und biomedizinische Ordnungssysteme.

A variety of rich terminology systems, such as thesauri, classifications, nomenclatures and ontologies support information and knowledge processing in health care and biomedical research. Nevertheless, human language, manifested as individually written texts, persists as the primary carrier of information, in the description of disease courses or treatment episodes in electronic medical records, and in the description of biomedical research in scientific publications. In the context of the discussion about big data in biomedicine, we hypothesize that the abstraction of the individuality of natural language utterances into structured and semantically normalized information facilitates the use of statistical data analytics to distil new knowledge out of textual data from biomedical research and clinical routine. Computerized human language technologies are constantly evolving and are increasingly ready to annotate narratives with codes from biomedical terminology. However, this depends heavily on linguistic and terminological resources. The creation and maintenance of such resources is labor-intensive. Nevertheless, it is sensible to assume that big data methods can be used to support this process. Examples include the learning of hierarchical relationships, the grouping of synonymous terms into concepts and the disambiguation of homonyms. Although clear evidence is still lacking, the combination of natural language technologies, semantic resources, and big data analytics is promising.

Feeding the human brain model.

The goal of the Human Brain Project is to develop, during the next decade, an infrastructure capable of simulating a draft human brain model based on available experimental data. One of the key issues is therefore to integrate and make accessible the experimental data necessary to constrain and fully specify this model. The required data covers many different spatial scales, ranging from the molecular scale to the whole brain and these data are obtained using a variety of techniques whose measurements may not be directly comparable. Furthermore, these data are incomplete, and will remain so at least for the coming decade. Here we review new neuroinformatics techniques that need to be developed and applied to address these issues.

COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". OBJECTIVES: In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. METHODS: We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. RESULTS: The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. CONCLUSIONS: The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective.

The paper presents an overview of approaches to encoding uncertain causal knowledge in formal ontologies and demonstrates how these approaches can be used in a semantic-driven application for public health using the Population Health Record (PopHR) platform as an example.

Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

OBJECTIVE: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. MATERIALS AND METHODS: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. RESULTS: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. CONCLUSIONS: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

Extending VIVO ontology to represent research and educational resources in an academic biomedical informatics department.

The increasing need for interdisciplinary team sciences makes it vital for academic research departments to publicize their research and educational resources as part of "linked data" on the semantic web to facilitate research networking and recruitment. We extended an open-source ontology, VIVO, to represent the research and educational resources in an academic biomedical informatics department to enable ontology-based information storage and retrieval. Using participatory design methods, we surveyed representative types of visitors to the department web site to understand their information needs, and incorporated these needs into the ontology design. We added 114 classes and 186 properties to VIVO. Generalizability and scalability are the measures used in our theoretical evaluation.

Constructing mammography examination process ontology using affinity diagram and hierarchical task analysis.

Mammography is complex and difficult for beginner radiologic technologists (RTs) because knowledge and technical skills rely on one's experience, and it is often difficult for experienced RTs to verbally explain the process to co-workers or beginners. The purpose of this study was to construct a mammography examination process ontology for knowledge sharing among RTs and propose a new ontology construction method using an affinity diagram (AD) and hierarchical task analysis (HTA). First, tasks collected by brainstorming were clustered and connected using the AD. Subsequently, a hierarchical structure was constructed based on the clusters and relations determined in the AD. Finally, a mammography process ontology was determined based on the relations noted in the AD and HTA. As a result, the ontology contained 203 classes and 669 relations.