Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.

ABSTRACT:

Objective

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

Materials and methods

Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples fro

SUBMITTER: Parr SK 

PROVIDER: S-EPMC7646911 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature