Abstract
Radiological diagnosis requires integration of imaging observations with patient factors, such as age, sex, and medical history. Imaging manifestations of a disease can be highly variable; conversely, one imaging finding may suggest several possible causes. To account for the inherent uncertainty of radiological diagnosis, this report explores the integration of probability data with an ontology of radiological diagnosis. The Radiology Gamuts Ontology (RGO) incorporates 16,839 entities that define diseases, interventions, and imaging observations of relevance to diagnostic radiology. RGO’s 55,564 causal (“may cause”) relationships link disorders and their potential imaging manifestations. From a cohort of 1.7 million radiology reports on more than 1.3 million patients, the frequency of individual RGO entities and of their pairwise co-occurrence was identified. These data allow estimation of conditional probabilities of pairs of entities. A user interface enables one to traverse the ontology’s network of causal relations with associated conditional-probability data. The system generates Bayesian network models that integrate an entity’s age and sex distribution with its causally related conditions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bodenreider, O.: Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform 67–79 (2008). https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18660879/
Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 37, W170-173 (2009). https://doi.org/10.1093/nar/gkp440
Filice, R.W., Kahn, C.E.: Biomedical Ontologies to Guide AI Development in Radiology. J. Digit. Imaging 34(6), 1331–1341 (2021). https://doi.org/10.1007/s10278-021-00527-1
Budovec, J.J., Lam, C.A., Kahn, C.E., Jr.: Radiology Gamuts Ontology: differential diagnosis for the Semantic Web. Radiographics 34, 254–264 (2014). https://doi.org/10.1148/rg.341135036
Kleinberg, S., Hripcsak, G. A review of causal inference for biomedical informatics. J Biomed Inform 44(6):1102–12 (2011) https://doi.org/10.1016/j.jbi.2011.07.001
Filice, R.W., Kahn, C.E. Jr.: Integrating an ontology of radiology differential diagnosis with ICD-10-CM, RadLex, and SNOMED CT. J Digit Imaging 32, 206–210 (2019). https://doi.org/10.1007/s10278-019-00186-3
Druzdzel, M.J.: SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: a development environment for graphical decision-theoretic models. In: AAAI Proceedings, pp. 902–903. AAAI, Washington, DC (1999). https://www.aaai.org/Papers/AAAI/1999/ AAAI99–129.pdf
Díez, F.J.: Parameter adjustment in Bayes networks. The generalized noisy OR-gate. In: Uncertainty in Artificial Intelligence In: Proceedings of the Ninth Conference, pp. 99–105. Morgan Kaufmann, San Mateo, CA (1993).https://doi.org/10.1016/B978-1-4832-1451-1.50016-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kahn, C. (2023). Integrating Ontological Knowledge with Probability Data to Aid Diagnosis in Radiology. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-34344-5_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34343-8
Online ISBN: 978-3-031-34344-5
eBook Packages: Computer ScienceComputer Science (R0)