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Having received a flu shot is a discrete measure. What we care about at the individual level is their response to that shot, which is continuous, minimally some

by tgbugs 6y ago

Having received a flu shot is a discrete measure. What we care about at the individual level is their response to that shot, which is continuous, minimally something like P(got-flu | got-flu-shot). What we care about at the population level is the probability that someone actually got the shot given that the records says that they did and the probability that they did get a flu shot given that the records say they didn't or that the records are absent. The conceptual data model for vaccination in the US is a table with ~370 million rows and a column for each vaccine with the type being the date the individual last received that vaccine or a null. It is nearly impossible to construct this table with any reasonable level of data quality. Even measurements of height are subject to error. The question is whether the error is larger than the level required for some particular use case. Thus it is not that height is somehow more discrete (height varies over the course of the day due to gravity), it is that our uses for it aren't affected by the very low uncertainty that its measurements entail. However, if you are building a spacesuit for someone and use a measurement of height taken at the end of the day in a gravitation field, they will be extremely uncomfortable when they get taller in space. Thus even when there are measurements which are nominally discrete, if the uncertainty is greater than that of the use case, then the fact that it is nominally discrete doesn't matter.

zby 6y ago
The question the article is about is 'can we deduce that a condition is discrete when we cannot measure it directly, only via symptoms'. If we could measure it directly - than we would obviously get a discrete distribution - but lets assume we we cannot do it. For example lets assume that we don't have the hi-tech flu tests and we can only measure temperature, frequency of cough, and some more subjective stuff like muscle and body aches, sore throat etc. These symptoms are not discrete - if we measure them in the whole population we shell not get any discrete distribution. So the fact that we can still deduce that there is a discrete condition underlying these continuos distributions is surprising - but apparently there are methods for that and one of them is called MAXCOV and it is briefly described in the article. Also - what is the author conflating - can you explain what you mean by that?
- tgbugs 6y ago
  In his language he conflates the diagnostic code with the etiology. He tries to avoid this, but is missing the concept for etiology, so he winds up in a situation where he can't talk about the relationship between the diagnosis and what is really going on, because there is only the question of whether the diagnosis is "real." The answer is that yes, the diagnosis is always real, but it is not what we care about if we want to understand what is really going on. This is why I said he is on the right track. In his ADHD example he has nearly all the parts, diagnosis and symptoms, but is missing the free variable for the etiology. You can see this because if he had this concept then it would be clear that there is no cure for ADHD, it isn't even clear that it is a "problem" except in the context of modern civilization, and the etiology is a fundamental difference in the structure of the brain, not something you can "cure."

by tgbugs 6y ago

zby 6y ago

The question the article is about is 'can we deduce that a condition is discrete when we cannot measure it directly, only via symptoms'. If we could measure it directly - than we would obviously get a discrete distribution - but lets assume we we cannot do it. For example lets assume that we don't have the hi-tech flu tests and we can only measure temperature, frequency of cough, and some more subjective stuff like muscle and body aches, sore throat etc. These symptoms are not discrete - if we measure them in the whole population we shell not get any discrete distribution. So the fact that we can still deduce that there is a discrete condition underlying these continuos distributions is surprising - but apparently there are methods for that and one of them is called MAXCOV and it is briefly described in the article. Also - what is the author conflating - can you explain what you mean by that?

tgbugs 6y ago
In his language he conflates the diagnostic code with the etiology. He tries to avoid this, but is missing the concept for etiology, so he winds up in a situation where he can't talk about the relationship between the diagnosis and what is really going on, because there is only the question of whether the diagnosis is "real." The answer is that yes, the diagnosis is always real, but it is not what we care about if we want to understand what is really going on. This is why I said he is on the right track. In his ADHD example he has nearly all the parts, diagnosis and symptoms, but is missing the free variable for the etiology. You can see this because if he had this concept then it would be clear that there is no cure for ADHD, it isn't even clear that it is a "problem" except in the context of modern civilization, and the etiology is a fundamental difference in the structure of the brain, not something you can "cure."