Bayes’ Theorem in Healthcare
The general population has the perspective that medical physicians always have the correct answer to any problem – but can they really fulfill those expectations?
As technology has advanced the medical field in the modern age, so has increased the plethora of tests and subsequently the need for correct test analysis. With the growing data to dissect, genes to sequence, and biological systems to model, math and probability have become prominent in the common physician-patient interaction. Doctors and medical students have show to be prone to error in interpreting these probabilities of risk. As the article describes, imagine two pieces of information: 1) a test that is 95% accurate for a disease and that 2) the disease investigated by the test affects .1 percent of the population. If the test was positive, what would the patients chance of the disease be?
For many – including the doctors and medical students – their intuition would say 95%, when in fact those with a background in math training would see that it is intact only 2%. Having a 5 percent error rate would mean that if 1000 people took the test, 50 would test positive; only one of them of would be actually affected as the disease affects .1% of the population.
In class, we explored the Bayes’ theorem, which describes the probability of an event based on the conditions related to the event. As the study of networks can be applied in multiple dimensions of society, it happens to find itself also in the field of medicine. Doctors are constantly diagnosing with a wealth of probabilities that interconnect, thus the push for better understanding the Bayes’ theorem would allow a better basis of diagnosis rather than the human intuition which is more prone to error.
http://www.taipeitimes.com/News/feat/archives/2016/05/09/2003645831