Ideas on not just implementation but also the project's mission and "end-product".
What's the point?:
More data is being generated every day, and yet scientific findings are
published as articles, in text form. They are not linked in any physical or
computational way. There is no way to meaningfully or adequately get a sense
of the entire scientific knowledge in an area without having to read through
hundreds, maybe thousands of articles.
We need some way to aggregrate these results... link them together so that
they are a whole, a single "physical" (as stored on a computer) object.
Scientific studies all have pros and cons, not just in study design, but
also in the study population.. they are often very different and hard to
completely translate to the wider public.
We need some way to determine how generalizable results are in the wider
context,
Sometimes, researchers want to see, before running a study, how particular
variables or biomarkers associate with a disease. Or maybe policy makers or
public health officials or citizen scientists want to see how changing one thing
will influence health in a quantifiable and real way, not just "increases
risk", etc. How much risk? And in an interactive, digital, quantitative way
(e.g. through a web interface or running a computer program).
We need some way to simulate possible scenarios, influenced by real-world
data.
What will it look it?:
The simulation will contain at least two components:
A "store" of real world data/scientific results to draw from and
inform the simulation. This is the aggregate of as much scientific
knowledge as possible. This could be the regression models or model
results of a study or something similar...
Infrastructure to use the real-world data to generate random
simulations of possible scenarios and what is most likely.
How will it be used?:
Researchers could use this as a "first pass resource" when exploring or
learning about current knowledge in the area. Using the first component, they
can get a sense of what is currently known.
Researchers could use the simulation to identify areas that are under
researched or that have not been researched
Policy makers, public health officials, the media could use these
simulations to test how certain things or conditions could influence health
and base their decisions on these results
Researchers could use this as a way explore possible research questions and
run pilot studies to see how their results compare to the simulated results.
Implementation:
(Came from discussion with @danielwitte)
Some possible ways to implement these:
Traditional MC models, using baseline values that continuously predict
possible future values.
Typical health economics models?
Trajectory based, as in predicted values will depend not just on baseline
values, but also on how variables operate as trajectories (e.g. weight over
time).
Adversarial neural networks. Have a "generator" who continually creates random
"correlation matrices" (or something similar) and a "discriminator" who chooses
which of a mix of real data/results vs the fake data/results. They compete in
a zero-sum game, until the "discriminator" has a harder time guessing real
vs fake. (this one seems the most promising).
This method could be generalizable to several situations: simulating
from child to adult or from a population at any given time point and
followed over time
This will learn from new data/results that come in, continually building
a database of "fake worlds" that improve over time.
Write in this issue ideas for implementing and developing this project. This is brainstorming so nothing is final here.