Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In the naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Towards Interoperability in E-health Systems: a three-dimensional approach based on standards and semantics

Proceedings of: HEALTHINF 2009 (International Conference on Helath Informatics), Porto (Portugal), January 14-17, 2009, is part of BIOSTEC (Intemational Joint Conference on Biomedical Engineering Systems and Technologies)The interoperability problem in eHealth can only be addressed by mean of combining standards and technology. However, these alone do not suffice. An appropiate framework that articulates such combination is required. In this paper, we adopt a three-dimensional (information, conference and inference) approach for such framework, based on OWL as formal language for terminological and ontological health resources, SNOMED CT as lexical backbone for all such resources, and the standard CEN 13606 for representing EHRs. Based on tha framewok, we propose a novel form for creating and supporting networks of clinical terminologies. Additionally, we propose a number of software modules to semantically process and exploit EHRs, including NLP-based search and inference, wich can support medical applications in heterogeneous and distributed eHealth systems.This work has been funded as part of the Spanish nationally funded projects ISSE (FIT-350300-2007-75) and CISEP (FIT-350301-2007-18). We also acknowledge IST-2005-027595 EU project NeO

SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

A Core Reference Hierarchical Primitive Ontology for Electronic Medical Records Semantics Interoperability

Currently, electronic medical records (EMR) cannot be exchanged among hospitals, clinics, laboratories, pharmacies, and insurance providers or made available to patients outside of local networks. Hospital, laboratory, pharmacy, and insurance provider legacy databases can share medical data within a respective network and limited data with patients. The lack of interoperability has its roots in the historical development of electronic medical records. Two issues contribute to interoperability failure. The first is that legacy medical record databases and expert systems were designed with semantics that support only internal information exchange. The second is ontological commitment to the semantics of a particular knowledge representation language formalism. This research seeks to address these interoperability failures through demonstration of the capability of a core reference, hierarchical primitive ontological architecture with concept primitive attributes definitions to integrate and resolve non-interoperable semantics among and extend coverage across existing clinical, drug, and hospital ontologies and terminologies

Decision support system for in-flight emergency events

Medical problems during flight have become an important issue as the number of passengers and miles flown continues to increase. The case of an incident in the plane falls within the scope of the healthcare management in the context of scarce resources associated with isolation of medical actors working in very complex conditions, both in terms of human and material resources. Telemedicine uses information and communication technologies to provide remote and flexible medical services, especially for geographically isolated people. Therefore, telemedicine can generate interesting solutions to the medical problems during flight. Our aim is to build a knowledge-based system able to help health professionals or staff members addressing an urgent situation by given them relevant information, some knowledge, and some judicious advice. In this context, knowledge representation and reasoning can be correctly realized using an ontology that is a representation of concepts, their attributes, and the relationships between them in a particular domain. Particularly, a medical ontology is a formal representation of a vocabulary related to a specific health domain. We propose a new approach to explain the arrangement of different ontological models (task ontology, inference ontology, and domain ontology), which are useful for monitoring remote medical activities and generating required information. These layers of ontologies facilitate the semantic modeling and structuring of health information. The incorporation of existing ontologies [for instance, Systematic Nomenclature Medical Clinical Terms (SNOMED CT)] guarantees improved health concept coverage with experienced knowledge. The proposal comprises conceptual means to generate substantial reasoning and relevant knowledge supporting telemedicine activities during the management of a medical incident and its characterization in the context of air travel. The considered modeling framework is sufficiently generic to cover complex medical situations for isolated and vulnerable populations needing some care and support services

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

Strengths and Limitations of Formal Ontologies in the Biomedical Domain

We propose a typology of representational artifacts for health care and life sciences domains and associate this typology with different kinds of formal ontology and logic, drawing conclusions as to the strengths and limitations for ontology in a description logics framework. The four types of domain representation we consider are: (i) lexico-semantic representation, (ii) representation of types of entities, (iii) representations of background knowledge, and (iv) representation of individuals. We advocate a clear distinction of the four kinds of representation in order to provide a more rational basis for using ontologies and related artifacts to advance integration of data and enhance interoperability of associated reasoning systems. We highlight the fact that only a minor portion of scientifically relevant facts in a domain such as biomedicine can be adequately represented by formal ontologies as long as the latter are conceived as representations of entity types. In particular, the attempt to encode default or probabilistic knowledge using ontologies so conceived is prone to produce unintended, erroneous models

Semantic annotation of clinical questionnaires to support personalized medicine

Tese de Mestrado, Bioinformática e Biologia Computacional, 2022, Universidade de Lisboa, Faculdade de CiênciasAtualmente estamos numa era global de constante evolução tecnológica, e uma das áreas que têm beneficiado com isso é a medicina, uma vez que com integração da vertente tecnológica na medicina, tem vindo a ter um papel cada vez mais importante quer do ponto de vista dos médicos quer do ponto de vista dos pacientes. Como resultado de melhores ferramentas que permitam melhorar o exercício das funções dos médicos, estão se a criar condições para que os pacientes possam ter um melhor acompanhamento, entendimento e atualização em tempo real da sua condição clínica. O setor dos Cuidados de Saúde é responsável pelas novidades que surgem quase diariamente e que permitem melhorar a experiência do paciente e o modo como os médicos podem tirar proveito da informação que os dados contêm em prol de uma validação mais célere e eficaz. Este setor tem gerado um volume cada vez mais maciço de dados, entre os quais relatórios médicos, registos de sensores inerciais, gravações de consultas, imagens, vídeos e avaliações médicas nas quais se inserem os questionários e as escalas clínicas que prometem aos pacientes um melhor acompanhamento do seu estado de saúde, no entanto o seu enorme volume, distribuição e a grande heterogeneidade dificulta o processamento e análise. A integração deste tipo de dados é um desafio, uma vez que têm origens em diversas fontes e uma heterogeneidade semântica bastante significativa; a integração semântica de dados biomédicos resulta num desenvolvimento de uma rede semântica biomédica que relaciona conceitos entre diversas fontes o que facilita a tradução de descobertas científicas ajudando na elaboração de análises e conclusões mais complexas para isso é crucial que se atinja a interoperabilidade semântica dos dados. Este é um passo muito importante que permite a interação entre diferentes conjuntos de dados clínicos dentro do mesmo sistema de informação ou entre sistemas diferentes. Esta integração permite às ferramentas de análise e interface com os dados trabalhar sobre uma visão integrada e holística dos dados, o que em última análise permite aos clínicos um acompanhamento mais detalhado e personalizado dos seus pacientes. Esta dissertação foi desenvolvida no LASIGE e em colaboração com o Campus Neurológico Sénior e faz parte de um grande projeto que explora o fornecimento de mais e melhores dados tanto a clínicos como a pacientes. A base deste projeto assenta numa aplicação web, o DataPark que possui uma plataforma que permite ao utilizador navegar por áreas clinicas entre as quais a nutrição, fisioterapia, terapia ocupacional, terapia da fala e neuropsicologia, em que cada uma delas que alberga baterias de testes com diversos questionários e escalas clínicas de avaliação. Este tipo de avaliação clínica facilita imenso o trabalho do médico uma vez que permite que sejam implementadas à distância uma vez que o paciente pode responder remotamente, estas respostas ficam guardadas no DataPark permitindo ao médico fazer um rastreamento do status do paciente ao longo do tempo em relação a uma determinada escala. No entanto o modo como o DataPark foi desenvolvido limita uma visão do médico orientada ao questionário, ou seja o médico que acompanha o paciente quando quer ter a visão do mesmo como um todo tem esta informação espalhada e dividida por estes diferentes questionários e tem de os ir ver a todos um a um para ter a noção do status do paciente. Esta dissertação pretende fazer face a este desafio construindo um algoritmo que decomponha todas as perguntas dos diferentes questionários e permita a sua integração semântica. Isto com o objectivo de permitir ao médico ter um visão holística orientada por conceito clínico. Procedeu-se então à extração de toda a base de dados presente no DataPark, sendo esta a fonte de dados sobre a qual este trabalho se baseou, frisando que originalmente existem muitos dados em Português que terão de ser traduzidos automaticamente. Com uma análise de alto nível (numa fase inicial) sobre os questionários da base de dados, iniciou-se a construção de um modelo semântico que pudesse descrever os dados presentes nos questionários e escalas. Assim de uma forma manual foi feito um levantamento de todos os conceitos clínicos que se conseguiu identificar num sub conjunto de questionários, mais concretamente 15 com os 5 mais respondidos em relação à Doença de parkinson, os 5 mais respondidos em relação à doença de AVC e os 5 mais respondidos que não estejam associados a uma única patologia em específico. Este modelo foi melhorado e evoluiu em conjunto com uma equipa de 12 médicos e terapeutas do CNS ao longo de 7 reuniões durante as quais foi levado a cabo um workshop de validação que permitiu dotar o modelo construído de uma fiabilidade elevada. Em paralelo procedeu-se à elaboração de 2 estudo: (i) um estudo que consistia em avaliar com qual ou quais ontologias se obtém a maior cobertura dos dados do sub conjunto de 15 questionários. A conclusão a que se chegou foi que o conjunto de ontologias que nos conferia mais segurança é constituído pelas ontologias LOINC, NCIT, SNOMED e OCHV, conjunto esse foi utilizado daqui em diante; (ii) outro estudo procurou aferir qual a ferramenta de tradução automática(Google Translator ou Microsoft Translator) que confere uma segurança maior, para isso procedeu-se à tradução completa de 3 questionários que apesar de estar na base de dados no idioma português, tem a sua versão original em inglês. Isto permitiu-nos traduzir estes 3 questionários de português para inglês e avaliar em qual das duas ferramentas se obteve uma melhor performance. O Microsoft Translator apresentou com uma diferença pequena um desempenho superior, sendo portanto a ferramenta de tradução automática escolhida para integrar o nosso algoritmo. Concluídos estes 2 estudos temos assim o conjunto de dados uniformizado numa só linguagem, e o conjunto de ontologias escolhidas para a anotação semântica. Para entender esta fase do trabalho há que entender que ontologias são poderosas ferramentas computacionais que consistem num conjunto de conceitos ou termos, que nomeiam e definem as entidades presentes num certo domínio de interesse, no ramo da biomedicina são designadas por ontologias biomédicas. O uso de ontologias biomédicas confere uma grande utilidade na partilha, recuperação e na extração de informação na biomedicina tendo um papel crucial para a interoperabilidade semântica que é exatamente o nosso objectivo final. Assim sendo procedeu-se à anotação semântica das questões do sub-conjunto de 15 questionários, uma anotação semântica é um processo que associa formalmente o alvo textual a um conceito/termo, podendo estabelecer desta forma pontes entre documentos/texto-alvos diferentes que abordam o mesmo conceito. Ou seja, uma anotação semântica é associar um termo de uma determinada ontologia a um conceito presente no texto alvo. Imaginando que o texto alvo são diferentes perguntas de vários questionários, é natural encontrar diferentes questões de diferentes áreas de diagnóstico que estejam conectados por termos ontológicos em comum. Depois da anotação completada é feita a integração do modelo semântico, com o algoritmo desenvolvido com o conjunto de ontologias e ainda com os dados dos pacientes. Desta forma sabemos que um determinado paciente respondeu a várias perguntas que abordam um mesmo conceito, essas perguntas estão interligadas semanticamente uma vez que têm o mesmo conceito mapeado. A nível de performance geral tanto os processos tradução como de anotação tiveram um desempenho aceitável, onde a nivel de tradução se atingiu 78% accuracy, 76% recall e uma F-mesure de 0.77 e ao nível da performance de anotação obteve-se 87% de anotações bem conseguidas. Portanto num cômputo geral consegue-se atingir o principal objectivo que era a obtenção holística integrada com o modelo semântico e os dados do DataPark(Questionários e pacientes).Healthcare is a multi-domain area, with professionals from different areas often collaborating to provide patients with the best possible care. Neurological and neurodegenerative diseases are especially so, with multiple areas, including neurology, psychology, nursing, physical therapy, speech therapy and others coming together to support these patients. The DataPark application allows healthcare providers to store, manage and analyse information about patients with neurological disorders from different perspectives including evaluation scales and questionnaires. However, the application does not provide a holistic view of the patient status because it is split across different domains and clinical scales. This work proposes a methodology for the semantic integration of this data. It developed the data scaffolding to afford a holistic view of the patient status that is concept-oriented rather than scale or test battery oriented. A semantic model was developed in collaboration with healthcare providers from different areas, which was subsequently aligned with existing biomedical ontologies. The questionnaire and scale data was semantically annotated to this semantic model, with a translation step when the original data was in Portuguese. The process was applied to a subset of 15 scales with a manual evaluation of each process. The semantic model includes 204 concepts and 436 links to external ontologies. Translation achieved an accuracy of 78%, whereas the semantic annotation achieved 87%. The final integrated dataset covers 443 patients. Finally, applying the process of semantic annotation to the whole dataset, conditions are created for the process of semantic integration to occur, this process consists in crossing all questions from different questionnaires and establishing a connection between those that contain the same annotation. This work allows healthcare providers to assess patients in a more global fashion, integrating data collected from different scales and test batteries that evaluate the same or similar parameters

Annotation analysis for testing drug safety signals using unstructured clinical notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records