Webinar: Differential Diagnosis of Lung Diseases
Recorded on April 13, 2018.
Webinar Overview
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.