Work

A Representativeness-informed Model for Research Record Selection from Electronic Medical Record Systems.

Year: 2022

Type: article

Abstract: Scientific and clinical studies have a long history of bias in recruitment of underprivileged and minority populations. This underrepresentation leads to inaccurate, inapplicable, and non-generalizable results. Electronic medical record (EMR) systems, which now drive much research, often poorly represent these groups. We introduce a method for quantifying representativeness using information theoretic measures and an algorithmic approach to select a more representative record cohort than random selection when resource limitations preclude researchers from reviewing every record in the database. We apply this method to select cohorts of 2,000-20,000 records from a large (2M+ records) EMR database at the Vanderbilt University Medical Center and assess representativeness based on age, ethnicity, race, and gender. Compared to random selection - which will on average mirror the EMR database demographics - we find that a representativeness-informed approach can compose a cohort of records that is approximately 5.8 times more representative.

Source: PubMed Central

Authors: Victor A Borza, Ellen Wright Clayton, Murat Kantarcioglu, Yevgeniy Vorobeychik, Bradley A Malin

Institutions: Vanderbilt University, The University of Texas at Dallas, Washington University in St. Louis

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: green