Abstract
We present a novel intelligent on-line system for home- monitoring of pregnant women that is developed to offer pregnant women personalised care. Present home-monitoring devices are restricted as they only collect physiological parameters and send them to a personal computer or cell phone for data storage and visualisation. In our work, however, we focus on the development of a probabilistic model that, based on the data available from different sources, is able to predict the evolution of a pregnancy disorder, here preeclampsia. The paper outlines the basic components of the system, describes in detail the decision-support model based on Bayesian networks, and report preliminary system’s application results using real patient data.
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References
Buttussi, F., Chittaro, L.: MOPET: A context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine 42(2), 153–163 (2008)
Cozman, F.G.: EBayes: embedded Bayesian networks (1999). http://www.cs.cmu.edu/~javabayes/EBayes/index.html
Duckitt, K., Harrington, D.: Risk factors for pre-eclampsia at antenatal booking: systematic review of controlled studies. BMJ 330(7491), 565–571 (2005)
Heckerman, D., Breese, J.S.: Causal independence for probability assessment and inference using Bayesian networks. IEEE Trans. on SMC–A 26(6), 826–831 (1996)
Ness, R.B., Roberts, J.M.: Epidemiology of pregnancy-related hypertension. In: Lindheimer, et al. (eds.) Chesley’s Hypertensive Disorders in Pregnancy. Academic Press, London (2009)
Rubel, P., Fayn, J., Simon-Chautemps, L., Atoui, H., Ohlsson, M., Telisson, D., et al.: New paradigms in telemedicine: Ambient intelligence, wearable, pervasive and personalized. Wearable eHealth Systems for Personalised Health Management: State of the Art and Future Challenges 108, 123–132 (2004)
Wu, W.H., Bui, A.A.T., Batalin, M.A., Au, L.K., Binney, J.D., Kaiser, W.J.: MEDIC: Medical embedded device for individualized care. Artificial Intelligence in Medicine 42(2), 137–152 (2008)
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© 2011 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Velikova, M., Lucas, P.J.F., Spaanderman, M. (2011). e-MomCare: A Personalised Home-Monitoring System for Pregnancy Disorders. In: Szomszor, M., Kostkova, P. (eds) Electronic Healthcare. eHealth 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 69. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23635-8_34
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DOI: https://doi.org/10.1007/978-3-642-23635-8_34
Publisher Name: Springer, Berlin, Heidelberg
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