A simple causal model for glucose metabolism in diabetes patients

Kinzang Chhogyal, Abhaya Nayak, Rolf Schwitter, Abdul Sattar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Employing the theory of belief change, we study the implementation of a simple causal model that can capture how the blood sugar level changes in a diabetes patient. For this purpose we use distance measures between worlds as the underlying mathematical foundation. Using a simple example in the medical domain we investigate how an agent with initially incomplete and/or incorrect knowledge can iteratively develop a simple causal model by interacting with an oracle that represents the complete and correct model of a diabetic patient.
Original languageEnglish
Title of host publicationProceedings of the First Australian Workshop on Artificial Intelligence in Health (AIH 2011), held in conjunction with the 24th Australasian Joint Conference on Artificial Intelligence (AI 2011), 5th December, Perth, Australia
PublisherThe Australian e-Health Research Centre
Pages9-18
Number of pages10
Publication statusPublished - 2011
EventAustralian Workshop on Artificial Intelligence in Health (1st : 2011) - Perth
Duration: 5 Dec 20115 Dec 2011

Workshop

WorkshopAustralian Workshop on Artificial Intelligence in Health (1st : 2011)
CityPerth
Period5/12/115/12/11

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Keywords

  • belief revision
  • belief update
  • causal models
  • glucose metabolism

Cite this

Chhogyal, K., Nayak, A., Schwitter, R., & Sattar, A. (2011). A simple causal model for glucose metabolism in diabetes patients. In Proceedings of the First Australian Workshop on Artificial Intelligence in Health (AIH 2011), held in conjunction with the 24th Australasian Joint Conference on Artificial Intelligence (AI 2011), 5th December, Perth, Australia (pp. 9-18). The Australian e-Health Research Centre.