Dynamic model updating (DMU) approach for statistical learning model building with missing data.

ABSTRACT:

Background

Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models.

Method and results

This study proposes a dynamic model updating (DMU) approach, a different strateg

SUBMITTER: Jain R 

PROVIDER: S-EPMC8086098 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature