Abstract:
There are different approaches to dealing
with missing data. A common one is by deleting
observations containing such data, but it is not applicable
when the volume of the data is limited. In this case, a
number of methods can be applied, such as Last
Observation Carried Forward and the like. But these
methods are not suitable when all data for a certain
parameter are missing. This paper describes a possibility of
addressing this issue in the case of time series of biomedical
data. Behind the method is the idea of the human body as a
complex system in which various parameters are correlated
and missing data can be inferred from the available data
using the estimated correlation. For this, machine learningbased linear regression models are built and used to recover
data describing the sepsis state. Finally, recovered data are
used to create a sepsis prediction system