83 research outputs found
Semantic annotation of electronic health records in a multilingual environment
Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de CiĂŞncias, 2017Os relatĂłrios de Radiologia descrevem os resultados dos procedimentos de radiografia e tĂŞm o potencial de ser uma fonte de informação Ăştil que pode trazer benefĂcios para os sistemas de saĂşde ao redor do mundo. No entanto, estes relatĂłrios sĂŁo geralmente escritos em texto livre e, portanto, Ă© difĂcil extrair automaticamente informação a partir deles. Contudo, o fato de que a maioria dos relatĂłrios estĂŁo agora digitalmente disponĂveis torna-os passĂveis de utilização de ferramentas de Prospeção de Texto (Text Mining). Outra vantagem dos relatĂłrios de Radiologia, que os torna mais suscetĂveis Ă utilização destas ferramentas, Ă© que mesmo se escritos em texto livre, eles sĂŁo geralmente bem estruturados. O problema Ă© que estas ferramentas sĂŁo principalmente desenvolvidas para InglĂŞs e os relatĂłrios sĂŁo geralmente escritos na lĂngua nativa do radiologista, que nĂŁo Ă© necessariamente o InglĂŞs. Isso cria um obstáculo para a partilha de informação de Radiologia entre diferentes comunidades, partilha esta importante para compreender e tratar eficazmente problemas de saĂşde. Existem basicamente duas soluções possĂveis para este problema. Uma solução Ă© traduzir o prĂłprio lĂ©xico que Ă© utilizado pela ferramenta de Prospeção de Texto que se pretende utilizar. A outra Ă© traduzir os prĂłprios relatĂłrios. Traduzir o lĂ©xico tem a vantagem de nĂŁo necessitar de tradução contĂnua, ou seja, depois de traduzir um lĂ©xico para, por exemplo, Espanhol, podemos usá-lo para processar tantos relatĂłrios EspanhĂłis nĂŁo traduzidas conforme necessário. No entanto, quando uma nova versĂŁo do lĂ©xico Ă© lançada as mudanças tambĂ©m precisam de ser traduzidas, caso contrário, o lĂ©xico traduzido ficaria desatualizado. Dada a crescente evolução de serviços de tradução hoje disponĂveis, neste trabalho Ă© avaliada a opção alternativa de traduzir os relatĂłrios e verificar a sua viabilidade. Esta abordagem tem a vantagem de que os relatĂłrios traduzidos seriam acessĂveis a qualquer mĂ©dico que entenda InglĂŞs e as ferramentas estado da arte de Prospeção de Texto focadas em texto em InglĂŞs podem ser aplicadas sem qualquer necessidade de adaptação. Se a tradução for feita por profissionais treinados em tradução de textos mĂ©dicos, provavelmente pode-se assumir que informação nĂŁo se perde no processo de tradução. Chamamos a este tipo de tradução Tradução Humana (Human Translation). Mas a utilização de tradutores especializados Ă© cara e nĂŁo escalável. Outra opção Ă© usar Tradução Automática (Machine Translation). NĂŁo obstante a menor qualidade da tradução, Ă© mais barata e mais viável em grande escala. Finalmente, uma opção que tenta obter o melhor dos dois mundos Ă© usar Tradução Automática seguida de PĂłs-Edição (Post-Edition) por humanos. Nesta abordagem, o texto Ă© automaticamente traduzido e, em seguida, a tradução Ă© corrigida por um humano. Mais barata do que a opção de Tradução Humana e com melhor qualidade do que a de Tradução Automática. A escolha de abordagem de tradução Ă© importante porque vai afetar a qualidade dos resultados das ferramentas de Prospeção de Texto. Atualmente nĂŁo há nenhum estudo disponĂvel publicamente que tenha fornecido evidĂŞncia quantitativa que auxilie a fazer esta escolha. Isto pode ser explicado pela falta de um corpus paralelo que poderia ser usado para estudar este problema. Este trabalho explora a solução de traduzir os relatĂłrios para InglĂŞs antes de aplicar as ferramentas de Prospeção de Texto, analisando a questĂŁo de qual a abordagem de tradução que deve ser usada. Com este fim, criei MRRAD (Multilingual Radiology Research Articles Dataset), um corpus paralelo de 51 artigos portugueses de investiga ção relacionados com Radiologia, e uma sĂ©rie de traduções alternativas (humanas, automáticas e semi-automáticas) para InglĂŞs. As versões originais dos artigos, em PortuguĂŞs, e as traduções humanas foram extraĂdas automaticamente da biblioteca online SciELO. As traduções automáticas foram obtidas utilizando os serviços da Yandex e da Google e traduções semi-automáticas atravĂ©s dos serviços da Unbabel. Este Ă© um corpus original que pode ser usado no avanço da investigação sobre este tema. Usando o MRRAD estudei que tipo de abordagem de tradução autom ática ou semi-automática Ă© mais eficaz na tarefa de Reconhecimento de Entidades (Named-Entity Recognition ) relacionados com Radiologia mencionadas na versĂŁo em InglĂŞs dos artigos. Estas entidades correspondem aos termos presentes no RadLex, que Ă© uma ontologia que se foca em termos relacionados com Radiologia. A tarefa de Reconhecimento de Entidades Ă© relevante uma vez que os seus resultados podem ser usadas em sistemas de Recuperação de Imagens (Image Retrieval ) e de Recuperação de Informação (Information Retrieval) e podem ser Ăşteis para melhorar Sistemas de Respostas a Perguntas (Question Answering). Para realizar o Reconhecimento de termos do RadLex utilizei a API do Open Biomedical Annotator e duas diferentes configurações do software NOBLE Coder. Assim, ao todo utilizei trĂŞs diferentes abordagens para identificar termos RadLex nos textos. A diferença entre as abordagens está em quĂŁo flexĂveis ou estritas estas sĂŁo em identificar os termos. Considerando os termos identificados nas traduções humanas como o padrĂŁo ouro (gold-standard ), calculei o quĂŁo semelhante a este padrĂŁo foram os termos identificados usando outras abordagens de tradução. Descobri que uma abordagem completamente automática de tradução utilizando o Google leva a micro F-Scores (entre 0,861 e 0,868, dependendo da abordagem de reconhecimento) semelhantes aos obtidos atravĂ©s de uma abordagem mais cara, tradução semi-automática usando Unbabel (entre 0,862 e 0,870). A abordagem de tradução utilizando os serviços da Yandex obteve micro F-Scores mais baixos (entre 0,829 e 0,831). Os resultados foram semelhantes mesmo no caso onde se consideraram apenas termos de RadLex pertences Ă s sub-árvores correspondentes a entidades anatĂłmicas e achados clĂnicos. Para entender melhor os resultados, tambĂ©m realizei uma análise qualitativa do tipo de erros encontrados nas traduções automáticas e semiautom áticas. A análise foi feita sobre os Falsos Positivos (FPs) e Falsos Negativos (FNs) cometidos pelas traduções utilizando Yandex, Google e Unbabel em 9 documentos aleatĂłrios e cada erro foi classificado por tipo. A maioria dos FPs e FNs sĂŁo explicados nĂŁo por uma tradução errada mas por outras causas, por exemplo, uma tradução alternativa que leva a uma diferença nos termos identificados. Poderia ser esperado que as traduções Unbabel tivessem muitos menos erros, visto que tĂŞm o envolvimento de humanos, do que as da Google, mas isso nem sempre acontece. Há situações em que erros sĂŁo atĂ© adicionados mesmo durante a etapa de PĂłs-Edição. Uma revisĂŁo dos erros faz-me propor que isso poderá ser devido Ă falta de conhecimento mĂ©dico dos editores (utilizadores responsáveis por fazer a PĂłs-Edição) atuais da Unbabel. Por exemplo, um stroke (acidente vascular cerebral) Ă© algo que ocorre no cĂ©rebro, mas num caso foi usado como algo que acontece no coração - alguĂ©m com algum conhecimento sobre a medicina nĂŁo faria este erro. Mas a verdade Ă© que a Unbabel atualmente nĂŁo se foca em conteĂşdo mĂ©dico. Prevejo que se eles o fizessem e investissem em crescer uma comunidade de utilizadores especialistas com melhor conhecimento da linguagem mĂ©dica, isso levaria a melhores resultados. Dito isto, os resultados deste trabalho corroboram a conclusĂŁo de que se engenheiros de software tiverem recursos financeiros limitados para pagar por Tradução Humana, ficarĂŁo melhor servidos se usarem um serviço de tradução automática como a Google em vez de um serviço que implementa PĂłs-Edição, como a Unbabel. É claro que talvez haja melhores serviços de Tradução Automática do que a Google ou melhores serviços de Tradução Automática + PĂłs-Edição do que a Unbabel oferece atualmente para o campo mĂ©dico, e isso Ă© algo que poderia ser explorado em trabalhos futuros. O corpus MRRAD e as anotações utilizadas neste trabalho podem ser encontradas em https://github.com/lasigeBioTM/MRRAD.Radiology reports describe the results of radiography procedures and have the potential of being an useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. Having this goal, I created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semiautomatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD, I studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms identified in human translations as the gold standard, I calculated how similar to this standard were the terms identified using other translation approaches (Yandex, Google and Unbabel). I found that a completely automatic translation approach using Google leads to micro F-Scores (between 0.861 and 0.868, depending on the identification approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results I also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. The MRRAD corpus and annotations used in this work can be found at https://github.com/lasigeBioTM/MRRAD
Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches
While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves
Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation
Background: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline.
Methods: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources.
Results: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%.
Conclusion: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain
A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology
To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog
A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology
To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology
Foreword
The aim of this Workshop is to focus on building and evaluating resources used to facilitate biomedical text mining, including their design, update, delivery, quality assessment, evaluation and dissemination. Key resources of interest are lexical and knowledge repositories (controlled vocabularies, terminologies, thesauri, ontologies) and annotated corpora, including both task-specific resources and repositories reengineered from biomedical or general language resources. Of particular interest is the process of building annotated resources, including designing guidelines and annotation schemas (aiming at both syntactic and semantic interoperability) and relying on language engineering standards. Challenging aspects are updates and evolution management of resources, as well as their documentation, dissemination and evaluation
A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer
The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. 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