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 | 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) |
| Editors | F. Calimeri, M. Dragoni, F. Stella |
| Publisher | CEUR-WS.org |
| Pages | 108-115 |
| Number of pages | 8 |
| Volume | 3880 |
| Publication status | Published - 22 Dec 2024 |
Funding
This publication is part of the project PersOn with file number P21-03 of the research programme Perspective which is financed by the Dutch Research Council (NWO)
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Bayesian networks
- Decision support
- Query response uncertainty
- Explainable AI
Fingerprint
Dive into the research topics of 'Bayesian networks in medicine: presenting query response uncertainty for decision support'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver