With the outbreak of the COVID-19 pandemic, reasoning about diseases has gone mainstream. No longer is it just healthcare professionals that perform differential diagnoses. Newspapers and social media have been publicizing charts that compare symptoms of COVID-19, the "regular" flu, and the common cold so individuals can potentially self-diagnose and reduce the burden on healthcare providers.
While a chart can list symptoms, it is not an "inference engine." Deliberate reasoning still has to happen in the mind of the self-diagnosing individual to reach a conclusion. That turns out to be the difficult part, as humans are ill-equipped to handle probabilistic inference from effect back to the cause, i.e., from symptom to disease.
In this webinar, we present Bayesian networks as a framework for encoding knowledge about diseases and symptoms. Given this knowledge base, we then use BayesiaLab's inference algorithms to update the probabilities of the potential conditions given the observed symptoms. A very similar model, the so-called "Visit Asia" network, was one of the earliest examples that illustrated the reasoning capabilities of Bayesian networks.
Please note that this webinar does not constitute medical advice. Although the example is based on current events, we focus solely on the reasoning process. Thus, all numerical values and probabilities shown in the presentation should be considered fictional.