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.
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Velikova, M., Lucas, P.J.F., Spaanderman, M. (2011). A Predictive Bayesian Network Model for Home Management of Preeclampsia. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_22
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DOI: https://doi.org/10.1007/978-3-642-22218-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22217-7
Online ISBN: 978-3-642-22218-4
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