1,859 research outputs found

    Utilização de dados estruturados na resposta a perguntas relacionadas com saúde

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    The current standard way of searching for information is through the usage of some kind of search engine. Even though there has been progress, it still is mainly based on the retrieval of a list of documents in which the words you searched for appear. Since the users goal is to find an answer to a question, having to look through multiple documents hoping that one of them have the information they are looking for is not very efficient. The aim of this thesis is to improve that process of searching for information, in this case of medical knowledge in two different ways, the first one is replacing the usual keywords used in search engines for something that is more natural to humans, a question in its natural form. The second one is to make use of the additional information that is present in a question format to provide the user an answer for that same question instead of a list of documents where those keywords are present. Since social media are the place where people replace the queries used on a search engine for questions that are usually answered by humans, it seems the natural place to look for the questions that we aim to provide with automatic answers. The first step to provide an answer to those questions will be to classify them in order to find what kind of information should be present in its answer. The second step is to identify the keywords that would be present if this was to be searched through the currently standard way. Having the keywords identified and knowing what kind of information the question aims to retrieve, it is now possible to map it into a query format and retrieve the information needed to provide an answer.Atualmente a forma mais comum de procurar informação é através da utilização de um motor de busca. Apesar de haver progresso os seus resultados continuam a ser maioritariamente baseados na devolução de uma lista de documentos onde estão presentes as palavras utilizadas na pesquisa, tendo o utilizador posteriormente que percorrer um conjunto dos documentos apresentados na esperança de obter a informação que procura. Para além de ser uma forma menos natural de procurar informação também é menos eficiente. O objetivo para esta tese é melhorar esse processo de procura de informação, sendo neste caso o foco a área da saúde. Estas melhorias aconteceriam de duas formas diferentes, sendo a primeira a substituição da query normalmente utilizada em motores de busca, por algo que nos é mais natural - uma pergunta. E a segunda seria aproveitar a informação adicional a que temos acesso apenas no formato de pergunta, para fornecer os dados necessários à sua resposta em vez de uma lista de documentos onde um conjunto de palavras-chave estão presentes. Sendo as redes sociais o local onde a busca por informação acontece através da utilização de perguntas, em substituição do que seria normal num motor de busca, pelo facto de a resposta nestas plataformas ser normalmente respondida por humanos e não máquinas. Parece assim ser o local natural para a recolha de perguntas para as quais temos o objetivo de fornecer uma ferramenta para a obtenção automática de uma resposta. O primeiro passo para ser possível fornecer esta resposta será a classificação das perguntas em diferentes tipos, tornando assim possível identificar qual a informação que se pretende obter. O segundo passo será identificar e categorizar as palavras de contexto biomédico presentes no texto fornecido, que seriam aquelas utilizadas caso a procura estivesse a ser feita utilizando as ferramentas convencionais. Tendo as palavras-chave sido identificadas e sabendo qual o tipo de informação que deverá estar presente na sua resposta. É agora possível mapear esta informação para um formato conhecido pelos computadores (query) e assim obter a informação pretendida.Mestrado em Engenharia Informátic

    Doctor of Philosophy

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    dissertationThe use of the various complementary and alternative medicine (CAM) modalities for the management of chronic illnesses is widespread, and still on the rise. Unfortunately, tools to support consumers in seeking information on the efficacy of these treatments are sparse and incomplete. The goals of this work were to understand CAM information needs in acquiring CAM information, assess currently available information resources, and investigate informatics methods to provide a foundation for the development of CAM information resources. This dissertation consists of four studies. The first was a quantitative study that aimed to assess the feasibility of delivering CAM-drug interaction information through a web-based application. This study resulted in an 85% participation rate and 33% of those patients reported the use of CAMs that had potential interactions with their conventional treatments. The next study aimed to assess online CAM information resources that provide information on drug-herb interactions to consumers. None of the sites scored high on the combination of completeness and accuracy and all sites were beyond the recommended reading level per the US Department of Health and Human Services. The third study investigated information-seeking behaviors for CAM information using an existing cohort of cancer survivors. The study showed that patients in the cohort continued to use CAM well into survivorship. Patients felt very much on their own in dealing with issues outside of direct treatment, which often resulted in a search for options and CAM use. Finally, a study was conducted to investigate two methods to semi-automatically extract CAM treatment relations from the biomedical literature. The methods rely on a database (SemMedDB) of semantic relations extracted from PubMed abstracts. This study demonstrated that SemMedDB can be used to reduce manual efforts, but review of the extracted sentences is still necessary due to a low mean precision of 23.7% and 26.4%. In summary, this dissertation provided greater insight into consumer information needs for CAM. Our findings provide an opportunity to leverage existing resources to improve the information-seeking experience for consumers through high-quality online tools, potentially moving them beyond the reliance on anecdotal evidence in the decision-making process for CAM

    The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

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    Background. 
The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

Description. 
SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers.

Conclusions.
SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies

    Representing and Redefining Specialised Knowledge: Medical Discourse

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    This volume brings together five selected papers on medical discourse which show how specialised medical corpora provide a framework that helps those engaging with medical discourse to determine how the everyday and the specialised combine to shape the discourse of medical professionals and non-medical communities in relation to both long and short-term factors. The papers contribute, in an exemplary way, to illustrating the shifting boundaries in today’s society between the two major poles making up the medical discourse cline: healthcare discourse at the one end, which records the demand for personalised therapies and individual medical services; and clinical discourse the other, which documents research into society’s collective medical needs

    The collection development manual of the Boston University Medical Center Alumni Medical Library

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    This publication represents the first Collection Development Manual (CDM) for the Alumni Medical Library. At the Alumni Medical Library, the Manual is used as a working reference for librarians; but the Library also uses this policy document to evaluate the effectiveness of its collection activities, and for the development of cooperative programs and services within Boston University, Boston Library Consortium, Massachusetts Health Sciences Network (MaHSLIN), NorthEast Regional Libraries (NERL). Librarians use the CDM as a model for their own collection development policymaking and planning. In collaboration with Alumni Medical Library, the Charles River Campus Libraries (Mugar, Law, Theology) review the policies in this document to determine collection boundaries

    The Evolution of myExperiment

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    The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'

    ConText, A Tool For the FDA\u27s Adverse Event Reporting System

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    This project is a continuation of the development of ConText, a Web Application that’s purpose is to assist FDA Agents in their work of analyzing FDA\u27s Adverse Event Reporting System reports and finding potential patterns between Drug Related incidents. Our focus was on updating numerous previously built functions of the project while also providing brand new features that would make navigating through reports more efficient. Our changes have improved upon the functionality of the previous prototype, making it easier for an agent to sort through the thousands of reports in their workload. This brings the FDA one step closer in realizing their goal in streamlining their pharmacovigilance and potentially saving countless lives

    Semantic user profiling techniques for personalised multimedia recommendation

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    Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

    Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

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    Consent forms serve as evidence of permissions granted by patients for clinical procedures. As the recognized value of biospecimens and health data increases, many clinical consent forms also seek permission from patients or their legally authorized representative to reuse residual clinical biospecimens and health data for secondary purposes, such as research. Such permissions are also granted by the government, which regulates how residual clinical biospecimens may be reused with or without consent. There is a need for increasingly capable information systems to facilitate discovery, access, and responsible reuse of residual clinical biospecimens and health data in accordance with these permissions. Semantic web technologies, especially ontologies, hold great promise as infrastructure for scalable, semantically interoperable approaches in healthcare and research. While there are many published ontologies for the biomedical domain, there is not yet ontological representation of the permissions relevant for reuse of residual clinical biospecimens and health data. The Informed Consent Ontology (ICO), originally designed for representing consent in research procedures, may already contain core classes necessary for representing clinical consent processes. However, formal evaluation is needed to make this determination and to extend the ontology to cover the new domain. This dissertation focuses on identifying the necessary information required for facilitating responsible reuse of residual clinical biospecimens and health data, and evaluating its representation within ICO. The questions guiding these studies include: 1. What is the necessary information regarding permissions for facilitating responsible reuse of residual clinical biospecimens and health data? 2. How well does the Informed Consent Ontology represent the identified information regarding permissions and obligations for reuse of residual clinical biospecimens and health data? We performed three sequential studies to answer these questions. First, we conducted a scoping review to identify regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data in the US, the permissions by which reuse of residual clinical biospecimens and health data may occur, and key issues that must be considered when interpreting these regulations and norms. Second, we developed and tested an annotation scheme to identify permissions within clinical consent forms. Lastly, we used these findings as source data for bottom-up modelling and evaluation of ICO for representation of this new domain. We found considerable overlap in classes already in ICO and those necessary for representing permissions to reuse residual clinical biospecimens and health data. However, we also identified more than fifty classes that should be added to or imported into ICO. These efforts provide a foundation for comprehensively representing permissions to reuse residual clinical biospecimens and health data. Such representation fills a critical gap for developing applications which safeguard biospecimen resources and enable querying based on their permissions for use. By modeling information about permissions in an ontology, the heterogeneity of these permissions at a range of levels (e.g., federal regulations, consent forms) can be richly represented using entity-relationship links and embedded rules of inference and inheritance. Furthermore, by developing this content in ICO, missing content will be added to the Open Biological and Biomedical Ontology (OBO) Foundry, enabling use alongside other widely adopted ontologies and providing a valuable resource for biospecimen and information management. These methods may also serve as a model for domain experts to interact with ontology development communities to improve ontologies and address gaps which hinder successful uptake.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162937/1/eliewolf_1.pd
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