ArticleJournal of the American Medical Informatics Association : JAMIA2018
Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management.Yearbook of medical informatics · 2019Pooled it
- Harmonizing Logical Observation Identifiers Names and Codes (LOINC) Codes and Units in Real-World Oncology Data: Method Development and Evaluation.JMIR medical informatics · 2026Article
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- Semantic enrichment of Pomeranian health study data using LOINC and WHO-FIC terminology mapping principles.JAMIA open · 2025Article
- Article
- Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.Annals of laboratory medicine · 2025Review
- AI Mapping of In-House Codes to LOINC Codes Using Laboratory Test Results Excluding Test Names: Toward International Sharing of Medical Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Why do probabilistic clinical models fail to transport between sites.NPJ digital medicine · 2024Review
- AnnoDash, a clinical terminology annotation dashboard.JAMIA open · 2023Article
- Mapping Clinical Documents to theAMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- BGLM: big data-guided LOINC mapping with multi-language support.JAMIA open · 2022Article
- Applications of machine learning in routine laboratory medicine: Current state and future directions.Clinical biochemistry · 2022Review
- Estimation of inter-laboratory reference change values from external quality assessment data.Biochemia medica · 2021Article
- Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINCJAMIA open · 2021Article
- System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration.Molecular diagnosis & therapy · 2021Review
- Automated Mapping of Real-world Oncology Laboratory Data to LOINC.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Building a Graph Representation of LOINCAMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 from both labeled and unlabeled datasets. Results: The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency. Conclusion: Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.