Dataset Information

A PubMed-wide associational study of infectious diseases.

ABSTRACT:

Background

Computational discovery is playing an ever-greater role in supporting the processes of knowledge synthesis. A significant proportion of the more than 18 million manuscripts indexed in the PubMed database describe infectious disease syndromes and various infectious agents. This study is the first attempt to integrate online repositories of text-based publications and microbial genome databases in order to explore the dynamics of relationships between pathogens and infectious diseases.

Methodology/principal findings

Herein we demonstrate how the knowledge space of infectious diseases can be computationally represented and quantified, and tracked over time. The knowledge space is explored by mapping of the infectious disease literature, looking at dynamics of literature deposition, zooming in from pathogen to genome level and searching for new associations. Syndromic signatures for different pathogens can be created to enable a new and clinically focussed reclassification of the microbial world. Examples of syndrome and pathogen networks illustrate how multilevel network representations of the relationships between infectious syndromes, pathogens and pathogen genomes can illuminate unexpected biological similarities in disease pathogenesis and epidemiology.

Conclusions/significance

This new approach based on text and data mining can support the discovery of previously hidden associations between diseases and microbial pathogens, clinically relevant reclassification of pathogenic microorganisms and accelerate the translational research enterprise.

SUBMITTER: Sintchenko V

PROVIDER: S-EPMC2835740 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

A PubMed-wide associational study of infectious diseases.

Sintchenko Vitali V Anthony Stephen S Phan Xuan-Hieu XH Lin Frank F Coiera Enrico W EW

PloS one 20100310 3

<h4>Background</h4>Computational discovery is playing an ever-greater role in supporting the processes of knowledge synthesis. A significant proportion of the more than 18 million manuscripts indexed in the PubMed database describe infectious disease syndromes and various infectious agents. This study is the first attempt to integrate online repositories of text-based publications and microbial genome databases in order to explore the dynamics of relationships between pathogens and infectious di ...[more]

PMID: 20224767

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Project description:Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design.To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians.We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians.A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues>2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach's Alpha=0.87), urgency and acuity (6 items, 11%, Cronbach's Alpha=0.67), and psychosocial behavior (4 items, 10%, Cronbach's alpha=0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared=0.13, p=0.61). There were no physician effects on the rating of perceived complexity.Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning.

Project description:Introduction: Infectious diseases have a major impact on morbidity and mortality in hospital. Microbial diagnosis remains elusive for most cases of suspected infection which impacts on the use of antibiotics. Rapid advances in genomic technologies combined with high-quality phenotypic data have great potential to improve the diagnosis, management and clinical outcomes of infectious diseases. The aim of the Bioresource in Adult Infectious Diseases (BioAID) is to provide a platform for biomarker discovery, trials and clinical service developments in the field of infectious diseases, by establishing a registry linking clinical phenotype to microbial and biological samples in adult patients who attend hospital with suspected infection. Methods and analysis: BioAID is a cohort study which employs deferred consent to obtain an additional 2.5mL RNA blood sample from patients who attend the Emergency Department (ED) with suspected infection when they undergo peripheral blood culture sampling. Clinical data and additional biological samples including DNA, serum and microbial isolates are obtained from BioAID participants during hospital admission. Participants are also asked to consent to be recalled for future studies. BioAID aims to recruit 10,000 patients from 5-8 sites across England. Since February 2014 >4000 individuals have been recruited to the study. The final cohort will be characterised using descriptive statistics including information on the number of cases that can be linked to biological and microbial samples to support future research studies. Ethical approval and section 251 exemption have been obtained for BioAID researchers to seek deferred consent from patients from whom a RNA specimen has been collected. Samples and meta-data obtained through BioAID will be made available to researchers worldwide following submission of an application form and research protocol. Conclusions: BioAID will support a range of study designs spanning discovery science, biomarker validation, disease pathogenesis and epidemiological analyses of clinical infection syndromes.

Dataset Information

A PubMed-wide associational study of infectious diseases.

Background

Methodology/principal findings

Conclusions/significance

Publications

A PubMed-wide associational study of infectious diseases.

Similar Datasets

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