Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Panzer, M.: Increasing patient findability of medical research : annotating clinical trials using standard vocabularies.
In: Bulletin of the Association for Information Science and Technology. 43(2017) no.2, S.40-43.
Abstract: Multiple groups at Mayo Clinic organize knowledge with the aid of metadata for a variety of purposes. The ontology group focuses on consumer-oriented health information using several controlled vocabularies to support and coordinate care providers, consumers, clinical knowledge and, as part of its research management, information on clinical trials. Poor findability, inconsistent indexing and specialized language undermined the goal of increasing trial participation. The ontology group designed a metadata framework addressing disorders and procedures, investigational drugs and clinical departments, adopted and translated the clinical terminology of SNOMED CT and RxNorm vocabularies to consumer language and coordinated terminology with Mayo's Consumer Health Vocabulary. The result enables retrieval of clinical trial information from multiple access points including conditions, procedures, drug names, organizations involved and trial phase. The jump in inquiries since the search site was revised and vocabularies were modified show evidence of success.
Inhalt: DOI: 10.1002/bul2.2017.1720430213.
Themenfeld: Semantische Interoperabilität
2Koopman, B. ; Zuccon, G. ; Bruza, P. ; Sitbon, L. ; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search.
In: Information Retrieval Journal. 19(2016) no.1, S.6-37.
Abstract: This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.
Inhalt: Vgl.: DOI: 10.1007/s10791-015-9268-9.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval ; Wissensrepräsentation
3Pinto, V.B. ; Rabelo, C.R. de Oliveira ; Girão, I.P.T.: SNOMED-CT as standard language for organization and representation of the information in patient records.
In: Knowledge organization. 41(2014) no.4, S.311-318.
Abstract: The Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), such as the Medical Subject Headings (MeSH) and the Health Sciences Descriptors (MeSH) is a standard for handling, organizing, representing and retrieval of information in the health context. It is structured, among other things, in 19 categories: clinical diagnosis/disease, procedures, observable entities, body structure, body, substance, biological and pharmaceutical products, sample, physical object, physical force, event, geographical or environmental location, social context, stages and scales, special concepts and qualifiers. We present research results carried out with patients' medical records in the Walter Cantidio University Hospital, at Federal University of Ceará. The line guiding this study seeks to answer the following question: what is the contribution of these categories to build a representation of the patient's medical records at the Department of Medical Records and Statistics (SAME), at the Walter Cantidio University Hospital (HUWC)? The objective of the research is to study the contribution of SNOMED-CT for the representation of information within those records. It is therefore an exploratory study supported by neofunctionalist method and content analysis, the physical structure of digitized records was analyzed at the SAME of the HUWC. Then we analyzed a corpus of two patient records with nine volumes, about 4000 pages corresponding to 777 Mb. The results and conclusions show that the hierarchical categories of SNOMED-CT may bring contributions to the representation of the charts, as it is a robust terminology based on ontology, contemplating the essence of the information recorded in these documents. Regarding the physical structure of the chart shows some similarities, and hence can contribute to information retrieval with higher added value, since it allows the use of pre and post-coordination as well as natural language, synonyms and acronyms.
Inhalt: Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_41_2014_4_f.pdf.
Anmerkung: Papers from I Congress of ISKO Spain and Portugal / XI Congress ISKO Spain, 7-9 November 2013, University of Porto.
4Shepherd, M. ; Sampalli, T.: Ontology as boundary object.
In: Categories, contexts and relations in knowledge organization: Proceedings of the Twelfth International ISKO Conference 6-9 August 2012, Mysore, India. Eds.: Neelameghan, A. u. K.S. Raghavan. Würzburg : Ergon Verlag, 2012. S.131-137.
(Advances in knowledge organization; vol.13)
Abstract: A lack of semantic interoperability in the multidisciplinary delivery of health care leads to poor health outcomes. This paper describes research that has lead to the development of an ontology based on SNOMED CT®. The ontology functions as a boundary object to bridge the semantic interoperability gap between members of multidisciplinary health care teams caring for patients with chronic diseases. Overall, there was strong agreement among the clinicians on the usefulness of the boundary object.
Themenfeld: Semantische Interoperabilität