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by kyouens
3743 days ago
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The issue of positive predictive value versus specificity comes up almost every day in my work as a pathologist. There is widespread misunderstanding, and in my own anecdotal experience, it very frequently results in unnecessary lab testing and misinterpretation of test results by clinicians. When I was a pre-med student, for some reason the prerequisites required a year of calculus. That succeeded in weeding out people who can't make an A in freshman calculus, but I'm not sure what else it accomplished. Calculus has little to do with the daily practice of medicine, unless you're a radiation oncologist or doing some hardcore research. A year of statistics would have served me and my patients much better. That goes double, given the current firehose of data that is part and parcel of the personalized medicine revolution. |
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My experience has been that big data is more about linear algebra, which is usually several classes beyond entry level calc or stats. You have to be able to reason about arbitrarily large collections of partial differential equations (albeit reduced to difference equations).
For example, if you want to talk about genomics, Durbin's Biologic Sequence Analysis is probably the most foundational text available. It introduces Bayes's theorem on page 8, has stuff that looks suspiciously like calculus on page 40, and is into Markov chains by page 48. They hold off on a formal treatment of entropy until about half-way through the book.
And for phylogenetics, the equivalent books is Felsenstein's Inferring Phylogenies. He introduces linear algebra before integration.
My favorite quote from Felsenstein, particularly germaine for pathologists (surely the taxonomists of medicine):
"Knowing exactly how many tree topologies ... is not particularly important. The point is that there are very large numbers of them. ... one use for the numbers was "to frighten taxonomists."
1. https://news.ycombinator.com/item?id=11326178