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Real-time Online Probabilistic Medical Computation using Bayesian Networks

EasyChair Preprint no. 2744

8 pagesDate: February 21, 2020


Advances in both computing power and novel Bayesian inference algorithms have enabled Bayesian Networks (BN) to be applied for decision-support in healthcare and other domains. This work presents CardiPro, a flexible, online application for interfacing with non-trivial causal BN models. Designed especially to make BN use easy for less-technical users like patients and clinicians, CardiPro provides near real-time probabilistic computation. CardiPro was developed as part of the PamBayesian research project ( and represents the first of a new generation of online BN-based applications that may benefit adoption of AI-based clinical decision-support.

Keyphrases: Bayesian networks, Healthcare, mHealth

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Scott Mclachlan and Haydn Paterson and Kudakwashe Dube and Evangelia Kyrimi and Eugene Dementiev and Martin Neil and Bridget Daley and Graham Hitman and Norman Fenton},
  title = {Real-time Online Probabilistic Medical Computation using Bayesian Networks},
  howpublished = {EasyChair Preprint no. 2744},

  year = {EasyChair, 2020}}
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