A predictive Bayesian network model for home management of preeclampsia

Authors:
Marina Velikova;Peter J. F. Lucas;Marc Spaanderman
Affiliations:
Radboud University Nijmegen, Institute for Computing and Information Sciences, The Netherlands;Radboud University Nijmegen, Institute for Computing and Information Sciences, The Netherlands;Maastricht University Medical Center, Department of Obstetrics and Gynecology, The Netherlands
Venue:
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Year:
2011

Citing 3
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Abstract

There is increasing consensus among health-care professionals and patients alike that many disorders can be managed, in principle, much better at home than in an out-patient clinic or hospital. In the paper, we describe a novel temporal Bayesian network model for the at home time-related development of preeclampsia, a common pregnancy-related disorder. The network model drives an android-based smartphone application that offers patients and their doctor insight into whether or not the disorder is developing positively--no clinical intervention required--or negatively--clinical intervention is definitely required. We discuss design considerations of the model and system, and review results obtained with actual patients.