This figure shows a 16 day time-series matrix from a patient with Ebola sepsis
There are three major (global) clinical transition points along this time matrix and several regional ones.
(A major clinical transition state as used here is one of “onset”, “worsening”, “phenotype change” or “recovery”)
It is pivotal to identify global transition states in clinical data especially in time relation to therapy . We have a large cohort with thousands of sepsis cases many having such transition states. Does anyone have advice re: the optimal conventional (non ML or AI) statistical methods to engage complex time matrix data like this and to define the probability of the occurrence of a global transition state.along time series matrix data sets?
Severe Ebola virus infection complicated by gram-negative septicemia. Kreuels B et.al N Engl J Med. 2015 Apr 2;372(14):1377.