The specific way to use data layers to predict.
They’ve opened a new restaurant right across the street.
At first. Then less so.
Because the problem is that this week I went there and my illusions were instantly trampled to the ground.
As soon as I saw that the menu had 328 dishes.
Thanks to our spidey sense, we all know that the more dishes, the less quality.
Not everything can be done well.
Not even geniuses like politicians can.
You can’t with restaurants and you can’t with health data.
That is why the monster companies that offer you all the data end up providing poor things.
I am referring to those that do the same for a survey of doctors, or a clinical trial, or give you data from the pharmacy.
In other words, they give it to you, but of poor quality and a little bit without knowing what to do with it afterwards.
Quite a bit.
A project that on day 1 has genomics, proteomics, clinical variables, imaging, etc., is not reliable either.
It is true that the more data layers, the greater the capacity to cluster patients.
Multimodality in itself is good.
The problem is putting it into practice.
That’s why it has to be treated with care. It doesn’t work either way.
You have to choose the right data layers, depending on the problem.
And not in any order, no. First some and then others.
All according to the objective to be achieved.
It is simple, but you have to do it well and be very methodical.
And if it is done, then yes.
Then you can do like that institution that upon seeing our database in a type of a cancer has convened the entire team in Europe and beyond so that each member can make their list of what they have called “disruptive questions”.
And for the record, I don’t like to disrupt. Neither disrupting nor arguing.
If you want to set up a multimodal big data project with feet and head, here.