I recently finished Pearl's "Book of Why" and iirc BNNs do not capture causal relationships between variables. So finding a strong association between a lab value and years of survival does not necessarily indicate that one led to the other.

In other words, a strongly associated lab or clinical value might actually be the result of a longer survival (or other compounding factors) and not the other way around.

Entirely not an expert, but this question sounds exactly like a problem for causal inference as described in the book. Of course, there is also the issue that only observational data seems to be available, so CI may not actually be possible. Maybe it's worth it to check out this entry point to the subject.

This is what I wanted to say too. Bayesian nets are good for identifying how strongly a parameter affects the outcome