Representing and coding the knowledge embedded in texts of Health Science Web published articles

Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface

OvidSP Medline-to-PubMed search filter translation: a methodology for extending search filter range to include PubMed's unique content

Background: PubMed translations of OvidSP Medline search filters offer searchers improved ease of access. They may also facilitate access to PubMed’s unique content, including citations for the most recently published biomedical evidence. Retrieving this content requires a search strategy comprising natural language terms (‘textwords’), rather than Medical Subject Headings (MeSH). We describe a reproducible methodology that uses a validated PubMed search filter translation to create a textword-only strategy to extend retrieval to PubMed’s unique heart failure literature. Methods: We translated an OvidSP Medline heart failure search filter for PubMed and established version equivalence in terms of indexed literature retrieval. The PubMed version was then run within PubMed to identify citations retrieved by the filter’s MeSH terms (Heart failure, Left ventricular dysfunction, and Cardiomyopathy). It was then rerun with the same MeSH terms restricted to searching on title and abstract fields (i.e. as ‘textwords’). Citations retrieved by the MeSH search but not the textword search were isolated. Frequency analysis of their titles/ abstracts identified natural language alternatives for those MeSH terms that performed less effectively as textwords. These terms were tested in combination to determine the best performing search string for reclaiming this ‘lost set’. This string, restricted to searching on PubMed’s unique content, was then combined with the validated PubMed translation to extend the filter’s performance in this database. Results: The PubMed heart failure filter retrieved 6829 citations. Of these, 834 (12%) failed to be retrieved when MeSH terms were converted to textwords. Frequency analysis of the 834 citations identified five high frequency natural language alternatives that could improve retrieval of this set (cardiac failure, cardiac resynchronization, left ventricular systolic dysfunction, left ventricular diastolic dysfunction, and LV dysfunction). Together these terms reclaimed 157/834 (18.8%) of lost citations. Conclusions: MeSH terms facilitate precise searching in PubMed’s indexed subset. They may, however, work less effectively as search terms prior to subject indexing. A validated PubMed search filter can be used to develop a supplementary textword-only search strategy to extend retrieval to PubMed’s unique content. A PubMed heart failure search filter is available on the CareSearch website (www.caresearch.com.au) providing access to both indexed and non-indexed heart failure evidence.This study was conducted as part of the work of the CareSearch Project. CareSearch is funded by the Australian Government Department of Health and Ageing

Knowledge-enhanced latent semantic indexing (KELSI): algorithms and applications

Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document ma-trix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. How-ever, for many knowledge domains (e.g., medicine) there are pre-existing semantic structures that could be used to organize and to categorize information. The goals of this study are to demonstrate how such semantic structures can be incorporated into the LSI vector space model and to measure their overall effect on query match-ing performance. The new approach, called Knowledge-Enhanced LSI (KELSI), is applied to documents in the OHSUMED medical abstracts using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall graphs and 11-point average precision values (P) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain over the original LSI model - 28% improvement for P=.01 and 100% improvement for P=.30. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matchs

Biodiversity informatics : organizing and linking information across the spectrum of life

This article has been accepted for publication in Briefings in Bioinformatics © 2007 The Author Published by Oxford University Press. All rights reserved. This is a pre-print, electronic version of an article published in Briefings in Bioinformatics 8 (2007) 347-357, doi:10.1093/bib/bbm037Biological knowledge can be inferred from three major levels of information: molecules, organisms, and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data; biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key “model” organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal meta-data element to link and integrate biodiversity data across a range of data sources

Adalimumab, etanercept and ustekinumab for treating plaque psoriasis in children and young people: systematic review and economic evaluation

Background: Psoriasis is a chronic inflammatory disease that predominantly affects the skin. Adalimumab (HUMIRA®, AbbVie, Maidenhead, UK), etanercept (Enbrel®, Pfizer, New York, NY, USA) and ustekinumab (STELARA®, Janssen Biotech, Inc., Titusville, NJ, USA) are the three biological treatments currently licensed for psoriasis in children. Objective: To determine the clinical effectiveness and cost-effectiveness of adalimumab, etanercept and ustekinumab within their respective licensed indications for the treatment of plaque psoriasis in children and young people. Data sources: Searches of the literature and regulatory sources, contact with European psoriasis registries, company submissions and clinical study reports from manufacturers, and previous National Institute for Health and Care Excellence (NICE) technology appraisal documentation. Review methods: Included studies were summarised and subjected to detailed critical appraisal. A network meta-analysis incorporating adult data was developed to connect the effectiveness data in children and young people and populate a de novo decision-analytic model. The model estimated the cost-effectiveness of adalimumab, etanercept and ustekinumab compared with each other and with either methotrexate or best supportive care (BSC), depending on the position of the intervention in the management pathway. Results: Of the 2386 non-duplicate records identified, nine studies (one randomised controlled trial for each drug plus six observational studies) were included in the review of clinical effectiveness and safety. Etanercept and ustekinumab resulted in significantly greater improvements in psoriasis symptoms than placebo at 12 weeks’ follow-up. The magnitude and persistence of the effects beyond 12 weeks is less certain. Adalimumab resulted in significantly greater improvements in psoriasis symptoms than methotrexate for some but not all measures at 16 weeks. Quality-of-life benefits were inconsistent across different measures. There was limited evidence of excess short-term adverse events; however, the possibility of rare events cannot be excluded. The majority of the incremental cost-effectiveness ratios for the use of biologics in children and young people exceeded NICE’s usual threshold for cost-effectiveness and were reduced significantly only when combined assumptions that align with those made in the management of psoriasis in adults were adopted. Limitations: The clinical evidence base for short- and long-term outcomes was limited in terms of total participant numbers, length of follow-up and the absence of young children. Conclusions: The paucity of clinical and economic evidence to inform the cost-effectiveness of biological treatments in children and young people imposed a number of strong assumptions and uncertainties. Health-related quality-of-life (HRQoL) gains associated with treatment and the number of hospitalisations in children and young people are areas of considerable uncertainty. The findings suggest that biological treatments may not be cost-effective for the management of psoriasis in children and young people at a willingness-to-pay threshold of £30,000 per quality-adjusted life-year, unless a number of strong assumptions about HRQoL and the costs of BSC are combined. Registry data on biological treatments would help determine safety, patterns of treatment switching, impact on comorbidities and long-term withdrawal rates. Further research is also needed into the resource use and costs associated with BSC. Adequately powered randomised controlled trials (including comparisons against placebo) could substantially reduce the uncertainty surrounding the effectiveness of biological treatments in biologic-experienced populations of children and young people, particularly in younger children. Such trials should establish the impact of biological therapies on HRQoL in this population, ideally by collecting direct estimates of EuroQol-5 Dimensions for Youth (EQ-5D-Y) utilities. Study registration: This study is registered as PROSPERO CRD42016039494. Funding: The National Institute for Health Research Health Technology Assessment programme

Review of Indexing Techniques Applied in Information Retrieval

Indexing is one of the important tasks of Information Retrieval that can be applied to any form of data, generated from the web, databases, etc. As the size of corpora increases, indexing becomes too time consuming and labor intensive, therefore, the introduction of computer aided indexer. A review of indexing techniques, both human and automatic indexing has been done in this paper. This paper gives an outline of the use of automatic indexing by discussing various hashing techniques including fuzzy finger printing and locality-sensitive hashing. Two different processes of matching that are used in automatic subject indexing are also reviewed. Accepting the need of automatic indexing in a possible replacement to manual indexing, studies in the development of automatic indexing tools must continu