Baye’s Rule in Medicine
Baye’s rule is an interesting theorem of probability that has applications in a multitude of fields, including game theory, statistical analyses, and even medicine. We can see today, how important Baye’s Rule is in medical tests and diagnoses. This article in particular states that the test is 99% accurate when it diagnosis a positive case and also 99% accurate when it diagnosis a negative case. However, what we don’t realize about these tests, is that they take into account prior data and information in determining whether a sample contains viral particles or not. This is consistent with the Bayesian inference that comes into play in many fields like cognitive science and probability, and it is that we will update our hypothesis as more information becomes available.
It is interesting that we apply this concept to diagnosing illnesses, because it means that your diagnosis not only depends on you and your symptoms, but also other information that you cannot control. By using Baye’s Rule, we can find factors that influence positive and negative test results. For example, we might be curious to find the probability of a test being positive given that it was done on a rainy day. We know that this should be the same as just the probability of the test being positive, but if we find that the values are not the same, then it implies that the weather could actually be an influence on test results. It is interesting how this theory can be applied simultaneously in medical contexts and in social contexts as discussed in class when talking about cascades.
https://www.theguardian.com/world/2021/apr/18/obscure-maths-bayes-theorem-reliability-covid-lateral-flow-tests-probability