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

Sharidan K. Parr | Zendy; Matthew S. Shotwell | Zendy; Alvin D. Jeffery | Zendy; Thomas A. Lasko | Zendy; Michael E. Matheny | Zendy

Open Access

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

Author(s) -

Sharidan K. Parr,

Matthew S. Shotwell,

Alvin D. Jeffery,

Thomas A. Lasko,

Michael E. Matheny

Publication year - 2018

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocy110

Subject(s) - computer science , identifier , artificial intelligence , context (archaeology) , data mining , code (set theory) , interoperability , database , test data , scalability , machine learning , natural language processing , programming language , paleontology , set (abstract data type) , biology , operating system

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.

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