[tdeck]

GraphViz has some useful graph schema languages that could be reused for something like this. There's DOT, a delightful DSL, and some kind of JSON format as well. You can then generate a bunch of different output formats and it will lay out the nodes for you.

[epistasis]

Of all the challenges with this, graph layout is beyond trivial. It does not rank as a problem, intellectual challenge, or even that interesting.

The challenges are all about what goes in the nodes, how to define it, how to standardize it across different institutions, how to compare it to what was tested in two different clinical trials, etc. And if the computerized process goes into clinical practice, how is that node and its contents robustly defined so that a clinician sitting with a patient can instantly understand what is meant by it's yes/no/multiple choice question in terms that have been used in recent years at the clinician's conferences.

Addressing the challenges of constructing the graph requires deep understanding of the terms, deep knowledge of how 10 different people from different cultural backgrounds and training locations interpret highly technical terms with evolving meanings, and deep knowledge of how people could misunderstand language or logic.

These guidelines codify evolving scientific knowledge where new conceptions of the disease get invented at every conference. It's all at the edge of science where every month and year we have new technology to understand more than we ever understood before, and we have new clinical trials that are testing new hypotheses at the edge of it.

Getting a nice visual layout is necessary, but in no way sufficient for what needs to be done to put this into practice.

[graphviz]

Not ... even that interesting?

[graphviz]

Modularity is an excellent way of attacking complex problems. We can all play with algorithms that can carry on realistic conversations and create synthetic 3D movies, because people worked on problems like making transistors the size of 10 atoms, figuring out how processors can predict branches with 99% accuracy, giving neural nets self-attention, deploying inexpensive and ridiculously fast networks all over the planet, and a lot of other stuff.

For many of us, curing cancer may someday become more important than almost anything else a computer can help us to do. It's just there are so many building blocks to solving truly complex problems; we must respect all that.