598 research outputs found

    Improving average ranking precision in user searches for biomedical research datasets

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    Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

    Mining and Representing Unstructured Nicotine Use Data in a Structured Format for Secondary Use

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    The objective of this study was to use rules, NLP and machine learning for addressing the problem of clinical data interoperability across healthcare providers. Addressing this problem has the potential to make clinical data comparable, retrievable and exchangeable between healthcare providers. Our focus was in giving structure to unstructured patient smoking information. We collected our data from the MIMIC-III database. We wrote rules for annotating the data, then trained a CRF sequence classifier. We obtained an f-measure of 86%, 72%, 69%, 80%, and 12% for substance smoked, frequency, amount, temporal, and duration respectively. Amount smoked yielded a small value due to scarcity of related data. Then for smoking status we obtained an f-measure of 94.8% for non-smoker class, 83.0% for current-smoker, and 65.7% for past-smoker. We created a FHIR profile for mapping the extracted data based on openEHR reference models, however in future we will explore mapping to CIMI models

    Automated Transformation of Semi-Structured Text Elements

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    Interconnected systems, such as electronic health records (EHR), considerably improved the handling and processing of health information while keeping the costs at a controlled level. Since the EHR virtually stores all data in digitized form, personal medical documents are easily and swiftly available when needed. However, multiple formats and differences in the health documents managed by various health care providers severely reduce the efficiency of the data sharing process. This paper presents a rule-based transformation system that converts semi-structured (annotated) text into standardized formats, such as HL7 CDA. It identifies relevant information in the input document by analyzing its structure as well as its content and inserts the required elements into corresponding reusable CDA templates, where the templates are selected according to the CDA document type-specific requirements

    Report on the 2015 NSF Workshop on Unified Annotation Tooling

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    On March 30 & 31, 2015, an international group of twenty-three researchers with expertise in linguistic annotation convened in Sunny Isles Beach, Florida to discuss problems with and potential solutions for the state of linguistic annotation tooling. The participants comprised 14 researchers from the U.S. and 9 from outside the U.S., with 7 countries and 4 continents represented, and hailed from fields and specialties including computational linguistics, artificial intelligence, speech processing, multi-modal data processing, clinical & medical natural language processing, linguistics, documentary linguistics, sign-language linguistics, corpus linguistics, and the digital humanities. The motivating problem of the workshop was the balkanization of annotation tooling, namely, that even though linguistic annotation requires sophisticated tool support to efficiently generate high-quality data, the landscape of tools for the field is fractured, incompatible, inconsistent, and lacks key capabilities. The overall goal of the workshop was to chart the way forward, centering on five key questions: (1) What are the problems with current tool landscape? (2) What are the possible benefits of solving some or all of these problems? (3) What capabilities are most needed? (4) How should we go about implementing these capabilities? And, (5) How should we ensure longevity and sustainability of the solution? I surveyed the participants before their arrival, which provided significant raw material for ideas, and the workshop discussion itself resulted in identification of ten specific classes of problems, five sets of most-needed capabilities. Importantly, we identified annotation project managers in computational linguistics as the key recipients and users of any solution, thereby succinctly addressing questions about the scope and audience of potential solutions. We discussed management and sustainability of potential solutions at length. The participants agreed on sixteen recommendations for future work. This technical report contains a detailed discussion of all these topics, a point-by-point review of the discussion in the workshop as it unfolded, detailed information on the participants and their expertise, and the summarized data from the surveys

    Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

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    The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

    Information extraction from medication leaflets

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    Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

    Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

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    abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

    Table-to-Text: Generating Descriptive Text for Scientific Tables from Randomized Controlled Trials

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    Unprecedented amounts of data have been generated in the biomedical domain, and the bottleneck for biomedical research has shifted from data generation to data management, interpretation, and communication. Therefore, it is highly desirable to develop systems to assist in text generation from biomedical data, which will greatly improve the dissemination of scientific findings. However, very few studies have investigated issues of data-to-text generation in the biomedical domain. Here I present a systematic study for generating descriptive text from tables in randomized clinical trials (RCT) articles, which includes: (1) an information model for representing RCT tables; (2) annotated corpora containing pairs of RCT table and descriptive text, and labeled structural and semantic information of RCT tables; (3) methods for recognizing structural and semantic information of RCT tables; (4) methods for generating text from RCT tables, evaluated by a user study on three aspects: relevance, grammatical quality, and matching. The proposed hybrid text generation method achieved a low bilingual evaluation understudy (BLEU) score of 5.69; but human review achieved scores of 9.3, 9.9 and 9.3 for relevance, grammatical quality and matching, respectively, which are comparable to review of original human-written text. To the best of our knowledge, this is the first study to generate text from scientific tables in the biomedical domain. The proposed information model, labeled corpora and developed methods for recognizing tables and generating descriptive text could also facilitate other biomedical and informatics research and applications
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