February 1, 2014

Surveillance for the prevention of chronic diseases through information association.

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Surveillance for the prevention of chronic diseases through information association.

BMC Med Genomics. 2014 Jan 30;7(1):7

Authors: Pollettini JT, Baranauskas JA, Ruiz ES, Pimentel MG, Macedo AA

BACKGROUND: Research on Genomic medicine has suggested that the exposure of patients to early life risk factorsmay induce the development of chronic diseases in adulthood, as the presence of premature riskfactors can influence gene expression. The large number of scientific papers published in thisresearch area makes it difficult for the healthcare professional to keep up with individual results andto establish association between them. Therefore, in our work we aim at building a computationalsystem that will offer an innovative approach that alerts health professionals about humandevelopment problems such as cardiovascular disease, obesity and type 2 diabetes.
METHODS: We built a computational system called Chronic Illness Surveillance System (CISS), which retrievesscientific studies that establish associations (conceptual relationships) between chronic diseases(cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. Toevaluate our approach, we submitted ten queries to CISS and to three other search engines(Google¿, Google Scholar¿ and Pubmed®)- the queries were composed of terms andexpressions from a list of risk factors provided by specialists.
RESULTS: CISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and asmaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the threeother systems. The results from the Friedman's test carried out with the post-hoc Holm procedure(95% confidence) for our system (control) versus the results for the three other engines indicate thatour system had the best performance in three of the categories (+), (-) and (+/-).This is an importantresult, since these are the most relevant categories for our users.
CONCLUSION: Our system should be able to assist researchers and health professionals in finding out relationshipsbetween potential risk factors and chronic diseases in scientific papers.

PMID: 24479447 [PubMed - as supplied by publisher]

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