Surveillance for the prevention of chronic diseases through information association

Abstract

BackgroundResearch on Genomic medicine has suggested that the exposure of patients to early life risk factors may induce the development of chronic diseases in adulthood, as the presence of premature risk factors can influence gene expression. The large number of scientific papers published in this research area makes it difficult for the healthcare professional to keep up with individual results and to establish association between them. Therefore, in our work we aim at building a computational system that will offer an innovative approach that alerts health professionals about human development problems such as cardiovascular disease, obesity and type 2 diabetes.MethodsWe built a computational system called Chronic Illness Surveillance System (CISS), which retrieves scientific studies that establish associations (conceptual relationships) between chronic diseases (cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. To evaluate our approach, we submitted ten queries to CISS as well as to three other search engines (Google™, Google Scholar™ and Pubmed®;) — the queries were composed of terms and expressions from a list of risk factors provided by specialists.ResultsCISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and a smaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the three other 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 that our system had the best performance in three of the categories (+), (-) and (+/-). This is an important result, since these are the most relevant categories for our users.ConclusionOur system should be able to assist researchers and health professionals in finding out relationships between potential risk factors and chronic diseases in scientific papers.

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

“…The structural architecture is presented here in Figure 1, the design as a class diagram is been depicted in [9] and the code is available on http://dcm.ffclrp.usp.br/hssf/) for download. HSSF has been designed as a result of the development of previous surveillance systems: Automatic-SL [2] and CISS [3] presented in Section 2. Later, HSSF was reused to develop CISS+ [15] and CISS-SW [16] introduced in Section 4.…”

“…Considering the purposes of the Automatic-SL [2] and CISS [3] systems, HSSF supports the following three CISS use cases with their requirements: (i) creation of a collection of scientific papers of a specific subject; (ii) retrieval of relevant scientific papers associated with clinical records; and (iii) classification of patients into risk groups.…”

“…To recommend information, our CISS retrieves scientific papers that relate chronic diseases to risk factors found in the patient's clinical records. By using our approach, healthcare professionals can create a clinical routine with families and set up the best possible growing conditions [3]. Instead of keywords, our CISS uses ontological terms to compose a more precise background epigenetic collection of documents to be related to clinical cases.…”

“…To associate children presenting developmental problems with the scientific literature and thus alert healthcare workers about health problems, we have also built the Chronic Illness Surveillance System (CISS) [3]. CISS aims to warm healthcare professionals of information regarding the patient's environmental exposure during early life (at conception, during fetal life, during infancy and during early childhood) that could influence gene expression and consequently induce changes in development due to variations in gene expression processes.…”

“…The CDO includes information about relations between genes and mutations, as well as health, nutrition and life history data 3. The Unified Medical Language System (UMLS), maintained by the US National Library of Medicine, is an important source of information that contributes to the processing and management of biomedical documents.…”