A Bayesian expert system for the analysis of an adverse drug reaction

Comparing risks of alternative medical diagnosis using Bayesian arguments

This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an alternative (non-invasive) test should have been used because it carried a lower risk. The case raises a number of general and widely applicable concerns about the decision-making process within the medical profession, including the ethics of informed consent, patient care liabilities when errors are made, and the research problem of focusing on 'true positives' while ignoring 'false positives'. An immediate concern is how best to present Bayesian arguments in such a way that they can be understood by people who would normally balk at mathematical equations. We feel it is possible to present purely visual representations of a non-trivial Bayesian argument in such a way that no mathematical knowledge or understanding is needed. The approach supports a wide range of alternative scenarios, makes all assumptions easily understandable and offers significant potential benefits to many areas of medical decision-making.

Probabilistic expert systems and graphical modelling: a case study in drug safety

Probabilistic expert systems are intended to provide reasoned guidance in complex environments characterized by extensive uncertainty . An explicit 'causal' model is constructed for the process being observed, in which an acyclic directed graph is used to express conditional independence assumptions about variables, and probability assessments specify a full joint probability distribution. The resulting graphical structure can cope with a range of issues that arise in realistic modelling. Here we consider a particular example of assessing the chance that a suspected adverse reaction is due to a particular drug under suspicion. The background biological knowledge provides an appropriate model and probability assessments are obtained from expert microbiologists. The model allows a variety of interpretations for ‘causality’. Details of the graphical and computational algorithms used to perform efficient calculations of conditional probabilities on complex graphical structures are provided and illustrated with the example. Further developments should allow updating of the risk parameters in the light of a series of case reports, and may form the basis for a flexible expert system for causality assessment and post-marketing surveillance.