Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity.

ABSTRACT:

Background

The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated.

Aims

Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring

SUBMITTER: Pikoula M 

PROVIDER: S-EPMC10270623 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature