Probabilities for a probabilistic network: a case study in oesophageal cancer

  • Authors:

  • L. C. van der Gaag;S. Renooij;C. L. M. Witteman;B. M. P. Aleman;B. G. Taal

  • Affiliations:

  • Institute of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands;Institute of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands;Institute of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands;Department of Radiation Oncology and Gastroenterology, The Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands;Department of Radiation Oncology and Gastroenterology, The Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2002

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Abstract

With the help of two experts in gastrointestinal oncology from The Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal cancer. The kernel of the system is a probabilistic network that describes the presentation characteristics of cancer of the oesophagus and the pathophysiological processes of invasion and metastasis. While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle. To overcome this obstacle, we designed a new method for eliciting probabilities from experts that combines the ideas of transcribing probabilities as fragments of text and of using a scale with both numerical and verbal anchors for marking assessments. In this paper, we report experiences with our method in eliciting the probabilities required for the oesophagus network. The method allowed us to elicit many probabilities in reasonable time. To gain some insight in the quality of the probabilities obtained, we conducted a preliminary evaluation study of our network, using data from real patients. We found that for 85% of the patients, the network predicted the correct cancer stage.