This is, in my view, the very opposite of a scientific question: it is a question rather about how to industrialise the AKI research enterprise. The core principle seems to be to deliver a generic outcome definition that liberates researchers from the burden of formulating sharply defined scientific theories specific to the particular intervention being ‘studied’.
This is indeed precisely the problem addressed by state-space modeling: there is some latent physiologic state X_t which — we theorise — evolves over time according to some ‘equations of motion’, and this state gets reflected in noisy observations Y_t (or measurements) which we may ‘filter’ to recover estimates of the underlying state variables. A basic application of these ideas can be found in this conference poster on tacrolimus dosing [1].
- Norris DC, Gohh RY, Akhlaghi F, Morrissey PE. Kalman filtering for tacrolimus dose titration in the early hospital course after kidney transplant. F1000Research. 2017;6. doi:10.7490/f1000research.1113595.1