A preeclampsia diagnosis approach using Bayesian networks
Rodrigues, J. R.
; Oliveira, A. O.
; Ramos, R. R.
; Saleem, K. S.
A preeclampsia diagnosis approach using Bayesian networks, Proc IEEE Communications Society IEEE International Conference on Communications ICC, Kuala Lumpur, Malaysia, Vol. CD, pp. 1 - 5, May, 2016.
Digital Object Identifier: 10.1109/ICC.2016.7510893
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Hypertension is the main cause of maternal death. Preeclampsia can affect pregnant women before or during pregnancy. Identification of patients with higher risk for preeclampsia allows some precautions that are taken to prevent its severe disease and subsequent complications. In medicine, there are different situations that deal with a large range of information, which needs a thorough assessment to be able to help experts in the decision-making process. Smart decision support systems allow grouping all existing information and finding pertinent information from it. Bayesian networks offer models that allow the information capture and handle situations of uncertainty. This paper proposes the construction of a system to support intelligent decision applied to the diagnosis of preeclampsia using Bayesian networks to help experts in the pregnant's care. The processes of qualitative and quantitative modeling to the construction of a network are also presented. The main contribution of this work includes the presentation of a Bayesian network built to help decision makers in moments of uncertainty in care of pregnant women.