A predictive Bayesian network model for home management of preeclampsia

Marina Velikova; Peter J.F. Lucas; Marc Spaanderman

doi:10.1007/978-3-642-22218-4_22

A predictive Bayesian network model for home management of preeclampsia

Marina Velikova^*, Peter J.F. Lucas, Marc Spaanderman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Intelligence in Medicine
Subtitle of host publication	13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings
Editors	Mor Peleg, Nada Lavrač, Carlo Combi
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	179-183
Number of pages	5
ISBN (Electronic)	978-3-642-22218-4
ISBN (Print)	978-3-642-22217-7
DOIs	https://doi.org/10.1007/978-3-642-22218-4_22
Publication status	Published - 2011
Externally published	Yes
Event	13th Conference on Artificial Intelligence in Medicine, AIME 2011 - Bled, Slovenia Duration: 2 Jul 2011 → 6 Jul 2011 Conference number: 13

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	6747
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Conference on Artificial Intelligence in Medicine, AIME 2011
Abbreviated title	AIME
Country/Territory	Slovenia
City	Bled
Period	2/07/11 → 6/07/11

Keywords

n/a OA procedure
Bayesian network
Conditional probability distribution
Bayesian network model
Home management
Conditional probability table

Access to Document

10.1007/978-3-642-22218-4_22

Cite this

Velikova, M., Lucas, P. J. F., & Spaanderman, M. (2011). A predictive Bayesian network model for home management of preeclampsia. In M. Peleg, N. Lavrač, & C. Combi (Eds.), Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings (pp. 179-183). (Lecture Notes in Computer Science; Vol. 6747). Springer. https://doi.org/10.1007/978-3-642-22218-4_22

Velikova, Marina ; Lucas, Peter J.F. ; Spaanderman, Marc. / A predictive Bayesian network model for home management of preeclampsia. Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings. editor / Mor Peleg ; Nada Lavrač ; Carlo Combi. Berlin, Heidelberg : Springer, 2011. pp. 179-183 (Lecture Notes in Computer Science).

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title = "A predictive Bayesian network model for home management of preeclampsia",

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.",

keywords = "n/a OA procedure, Bayesian network, Conditional probability distribution, Bayesian network model, Home management, Conditional probability table",

author = "Marina Velikova and Lucas, {Peter J.F.} and Marc Spaanderman",

year = "2011",

doi = "10.1007/978-3-642-22218-4_22",

language = "English",

isbn = "978-3-642-22217-7",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "179--183",

editor = "Mor Peleg and Nada Lavra{\v c} and Carlo Combi",

booktitle = "Artificial Intelligence in Medicine",

address = "Germany",

note = "13th Conference on Artificial Intelligence in Medicine, AIME 2011, AIME ; Conference date: 02-07-2011 Through 06-07-2011",

}

Velikova, M, Lucas, PJF & Spaanderman, M 2011, A predictive Bayesian network model for home management of preeclampsia. in M Peleg, N Lavrač & C Combi (eds), Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings. Lecture Notes in Computer Science, vol. 6747, Springer, Berlin, Heidelberg, pp. 179-183, 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, 2/07/11. https://doi.org/10.1007/978-3-642-22218-4_22

A predictive Bayesian network model for home management of preeclampsia. / Velikova, Marina; Lucas, Peter J.F.; Spaanderman, Marc.
Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings. ed. / Mor Peleg; Nada Lavrač; Carlo Combi. Berlin, Heidelberg: Springer, 2011. p. 179-183 (Lecture Notes in Computer Science; Vol. 6747).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Velikova, Marina

AU - Lucas, Peter J.F.

AU - Spaanderman, Marc

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N2 - 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.

AB - 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.

KW - n/a OA procedure

KW - Bayesian network

KW - Conditional probability distribution

KW - Bayesian network model

KW - Home management

KW - Conditional probability table

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U2 - 10.1007/978-3-642-22218-4_22

DO - 10.1007/978-3-642-22218-4_22

M3 - Conference contribution

SN - 978-3-642-22217-7

T3 - Lecture Notes in Computer Science

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BT - Artificial Intelligence in Medicine

A2 - Peleg, Mor

A2 - Lavrač, Nada

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ER -

Velikova M, Lucas PJF, Spaanderman M. A predictive Bayesian network model for home management of preeclampsia. In Peleg M, Lavrač N, Combi C, editors, Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011, Proceedings. Berlin, Heidelberg: Springer. 2011. p. 179-183. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-22218-4_22

A predictive Bayesian network model for home management of preeclampsia

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