Dealing with missing continuous biomedical data: a data recovery method for machine learning purposes

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