Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support

Janneke H. Bolt; Anna Berghuis; Arjen Hommersom; Marike Lombaers; Johanna M.A. Pijnenborg; Silja Renooij

Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support

Janneke H. Bolt, Anna Berghuis, Arjen Hommersom, Marike Lombaers, Johanna M.A. Pijnenborg, Silja Renooij

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

Abstract

Despite their good characteristics, tools based on Bayesian networks are not yet widely used in medical decision
support. Meeting the needs of the intended users is crucial for the acceptance of these tools, and clinical
involvement in their development is thus required to promote their use. During the development of a Bayesian
network-based tool for the prediction of lymph node metastases in patients with endometrial cancer, as one of
the users needs a measure of query response uncertainty was put forward. In this paper, we sustain meeting this
need by exploring options for the presentation of query response uncertainty. We consider the level of detail,
one-sided versus two-sided intervals and two different ’look-ahead’ options. The different options are illustrated
through a small example network.

Original language	English
Title of host publication	Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024)
Editors	Francesco Calimeri, Mauro Dragoni, Fabio Stella
Publisher	CEUR-WS.org
Volume	3880
Publication status	Published - 2024
Event	HC@AIxIA 2024: 3rd AIxIA Workshop on Artificial Intelligence For Healthcare - Bolzano, Italy Duration: 27 Nov 2024 → 28 Nov 2024 https://sites.google.com/unical.it/hcaixia2024/

Publication series

Series	CEUR Workshop Proceedings
ISSN	1613-0073

Conference

Conference	HC@AIxIA 2024
Country/Territory	Italy
City	Bolzano
Period	27/11/24 → 28/11/24
Internet address	https://sites.google.com/unical.it/hcaixia2024/

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https://ceur-ws.org/Vol-3880/paper10.pdfLicence: CC BY

Cite this

Bolt, J. H., Berghuis, A., Hommersom, A., Lombaers, M., Pijnenborg, J. M. A., & Renooij, S. (2024). Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support. In F. Calimeri, M. Dragoni, & F. Stella (Eds.), Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) (Vol. 3880). CEUR-WS.org. https://ceur-ws.org/Vol-3880/paper10.pdf

Bolt, Janneke H. ; Berghuis, Anna ; Hommersom, Arjen et al. / Bayesian Networks in Medicine : Presenting Query Response Uncertainty for Decision Support. Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024). editor / Francesco Calimeri ; Mauro Dragoni ; Fabio Stella. Vol. 3880 CEUR-WS.org, 2024. (CEUR Workshop Proceedings).

@inproceedings{3540b48e36e94d5a9b9f69eac474cabb,

title = "Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support",

abstract = "Despite their good characteristics, tools based on Bayesian networks are not yet widely used in medical decisionsupport. Meeting the needs of the intended users is crucial for the acceptance of these tools, and clinicalinvolvement in their development is thus required to promote their use. During the development of a Bayesiannetwork-based tool for the prediction of lymph node metastases in patients with endometrial cancer, as one ofthe users needs a measure of query response uncertainty was put forward. In this paper, we sustain meeting thisneed by exploring options for the presentation of query response uncertainty. We consider the level of detail,one-sided versus two-sided intervals and two different {\textquoteright}look-ahead{\textquoteright} options. The different options are illustratedthrough a small example network.",

author = "Bolt, {Janneke H.} and Anna Berghuis and Arjen Hommersom and Marike Lombaers and Pijnenborg, {Johanna M.A.} and Silja Renooij",

year = "2024",

language = "English",

volume = "3880",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

editor = "Francesco Calimeri and Mauro Dragoni and Fabio Stella",

booktitle = "Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024)",

address = "Germany",

note = "HC@AIxIA 2024 : 3rd AIxIA Workshop on Artificial Intelligence For Healthcare ; Conference date: 27-11-2024 Through 28-11-2024",

url = "https://sites.google.com/unical.it/hcaixia2024/",

}

Bolt, JH, Berghuis, A, Hommersom, A, Lombaers, M, Pijnenborg, JMA & Renooij, S 2024, Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support. in F Calimeri, M Dragoni & F Stella (eds), Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024). vol. 3880, CEUR-WS.org, CEUR Workshop Proceedings, HC@AIxIA 2024, Bolzano, Italy, 27/11/24. <https://ceur-ws.org/Vol-3880/paper10.pdf>

Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support. / Bolt, Janneke H.; Berghuis, Anna; Hommersom, Arjen et al.
Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024). ed. / Francesco Calimeri; Mauro Dragoni; Fabio Stella. Vol. 3880 CEUR-WS.org, 2024. (CEUR Workshop Proceedings).

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

TY - GEN

T1 - Bayesian Networks in Medicine

T2 - HC@AIxIA 2024

AU - Bolt, Janneke H.

AU - Berghuis, Anna

AU - Hommersom, Arjen

AU - Lombaers, Marike

AU - Pijnenborg, Johanna M.A.

AU - Renooij, Silja

PY - 2024

Y1 - 2024

N2 - Despite their good characteristics, tools based on Bayesian networks are not yet widely used in medical decisionsupport. Meeting the needs of the intended users is crucial for the acceptance of these tools, and clinicalinvolvement in their development is thus required to promote their use. During the development of a Bayesiannetwork-based tool for the prediction of lymph node metastases in patients with endometrial cancer, as one ofthe users needs a measure of query response uncertainty was put forward. In this paper, we sustain meeting thisneed by exploring options for the presentation of query response uncertainty. We consider the level of detail,one-sided versus two-sided intervals and two different ’look-ahead’ options. The different options are illustratedthrough a small example network.

AB - Despite their good characteristics, tools based on Bayesian networks are not yet widely used in medical decisionsupport. Meeting the needs of the intended users is crucial for the acceptance of these tools, and clinicalinvolvement in their development is thus required to promote their use. During the development of a Bayesiannetwork-based tool for the prediction of lymph node metastases in patients with endometrial cancer, as one ofthe users needs a measure of query response uncertainty was put forward. In this paper, we sustain meeting thisneed by exploring options for the presentation of query response uncertainty. We consider the level of detail,one-sided versus two-sided intervals and two different ’look-ahead’ options. The different options are illustratedthrough a small example network.

UR - https://sites.google.com/unical.it/hcaixia2024/program/workshop-program

M3 - Conference Article in proceeding

VL - 3880

T3 - CEUR Workshop Proceedings

BT - Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024)

A2 - Calimeri, Francesco

A2 - Dragoni, Mauro

A2 - Stella, Fabio

PB - CEUR-WS.org

Y2 - 27 November 2024 through 28 November 2024

ER -

Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support

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