Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support

IEEE Trans Vis Comput Graph. 2023 Aug;29(8):3602-3616. doi: 10.1109/TVCG.2022.3166071. Epub 2023 Jun 29.

Abstract

The development and validation of Clinical Decision Support Models (CDSM) based on Bayesian networks (BN) is commonly done in a collaborative work between medical researchers providing the domain expertise and computer scientists developing the decision support model. Although modern tools provide facilities for data-driven model generation, domain experts are required to validate the accuracy of the learned model and to provide expert knowledge for fine-tuning it while computer scientists are needed to integrate this knowledge in the learned model (hybrid modeling approach). This generally time-expensive procedure hampers CDSM generation and updating. To address this problem, we developed a novel interactive visual approach allowing medical researchers with less knowledge in CDSM to develop and validate BNs based on domain specific data mainly independently and thus, diminishing the need for an additional computer scientist. In this context, we abstracted and simplified the common workflow in BN development as well as adjusted the workflow to medical experts' needs. We demonstrate our visual approach with data of endometrial cancer patients and evaluated it with six medical researchers who are domain experts in the gynecological field.

MeSH terms

  • Bayes Theorem
  • Computer Graphics
  • Decision Support Systems, Clinical*
  • Humans