Webinar Series — Reasoning Under Uncertainty (Part 1): Differential Diagnosis of Diseases
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.
Please also see the next installment of this webinar series on reasoning under uncertainty on March 26: Differential Diagnosis of COVID-19 and Influenza-Like Conditions.
Please post all your questions and comments below.
Great presentation! Slides 17 and 18 show that p(Infected | Test = positive) = 50%, where sensitivity and specificity = 99.9%. Assuming same parameters for COVID19 tests, doesn't this mean that each "confirmed" COVID19 "case" is equally likely to be "COVID19" and "not COVID19"?
Along similar lines, a Medium.com article* cites a WHO study on China** to the effect that "if you come in contact with someone who tests positive for COVID-19 you have a 1–5% chance of catching it as well." Might this low perceived transmission rate be partly a function of false positives?