Preditive Models And Health Sciences: A Brief Analysis

  • Jair Sales Paulino, Msc 1. Master Program in Regional Sustainable Development (PRODER), Science and Technology Center, Universidade Federal do Cariri (UFCA), Juazeiro do Norte, Ceará, Brasil.
  • Crístenes Sanches Lucena Gomes, Fellow School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil.
  • Jucier Gonçalves Júnior School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil.
  • Micaelle Nayara Dias Rodrigues, Post Graduate Post graduation in Human Resources Management, Faculdade de Juazeiro do Norte (FJN), Juazeiro do Norte, Ceará, Brasil.
  • Ana Leice Silva Souza, Post Graduate Pós-Graduação em Consultoria e Auditoria em Gestão Financeira
  • João Vitor Cândido Pimentel, Fellow School of Medicine, Universidade Federal do Cariri (UFCA), Barbalha, Ceará, Brasil
  • Kelvin Alexandre de Oliveira Brito, Fellow Universidade Regional do Cariri (URCA), Juazeiro do Norte, Ceará. Brasil
  • Samuel Flavio Lima Saboia, Fellow Universidade Regional do Cariri (URCA), Juazeiro do Norte, Ceará. Brasil
  • Paulo Renato Alves Firmino, PhD Master Program in Regional Sustainable Development (PRODER), Science and Technology Center, Universidade Federal do Cariri (UFCA), Juazeiro do Norte, Ceará, Brasil.

Abstract

Background: Predictive Models are an important tool in event predicting and health planning. Despite this, there are few works focusing this area. Thus, the analysis of the real benefits of these models in Health Sciences is necessary to be performed.


Results: Predictive techniques largely evolved in second half of XX century. The development of AR, MA, ARMA, ARIMA and SARIMA models, through Box-Jenkins methodology, constitute a robust conjunct of mechanisms able to help in solution of epidemiological modeling problems, mainly in Health Sciences, once it allows to evaluate individual characteristics of living beings and its correlation with pathologies in the same space-time. Nevertheless, AR, MA and ARMA does not have tendency in seasonality, which weakens the analysis.


Conclusions: To predict the natural history of endemic/epidemic and its health-disease processes in a determined population is a sine que non condition to its adequate management in Public Health context and in adoption of affirmative measures concerning health promotion. Thus, the predictive models, with emphasis in ARIMA, SARIMA, Artificial Neural Networks and Formalism of Copulas are alternatives that can be feasible.

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Published
2017-07-02
How to Cite
PAULINO, Jair Sales et al. Preditive Models And Health Sciences: A Brief Analysis. International Archives of Medicine, [S.l.], v. 10, july 2017. ISSN 1755-7682. Available at: <http://imedicalsociety.org/ojs/index.php/iam/article/view/2271>. Date accessed: 19 nov. 2017. doi: https://doi.org/10.3823/2487.
Section
Epidemiology