Reporting of demographic data and representativeness in machine learning models using electronic health records.

ABSTRACT:

Objective

The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility.

Materials and methods

We searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019.

Results

Across the 164 studies reviewed, demographic variables were inconsistently reported and/or i

SUBMITTER: Bozkurt S 

PROVIDER: S-EPMC7727384 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature