ArticleJournal of the American Medical Informatics Association : JAMIA
A corpus-based approach for automated LOINC mapping.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Harmonizing Logical Observation Identifiers Names and Codes (LOINC) Codes and Units in Real-World Oncology Data: Method Development and Evaluation.JMIR medical informatics · 2026Article
- Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine.Proceedings. IEEE International Conference on Healthcare Informatics · 2025Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- BGLM: big data-guided LOINC mapping with multi-language support.JAMIA open · 2022Article
- Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINCJAMIA open · 2021Article
- Automated Mapping of Real-world Oncology Laboratory Data to LOINC.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective Study.JMIR medical informatics · 2019Article
- Interactive Machine Learning for Laboratory Data Integration.Studies in health technology and informatics · 2019Article
- Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Learning from the crowd while mapping to LOINC.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- A multi-part matching strategy for mapping LOINC with laboratory terminologies.Journal of the American Medical Informatics Association : JAMIAArticle
- Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure.Radiographics : a review publication of the Radiological Society of North America, IncReview
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2 authors.
Funding
Abstract
objectiveTo determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions.
methodsWe developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene. The models were validated by a random subsampling method that was repeated 20 times and that used 80/20 splits for training and testing, respectively. We also evaluated the performance of these models on all laboratory terms from three test institutions.
resultsFor the 20 iterations used for validation of our 80/20 splits Maxent and Lucene ranked the correct LOINC code first for between 70.5% and 71.4% and between 63.7% and 65.0% of local terms, respectively. For all laboratory terms from the three test institutions Maxent ranked the correct LOINC code first for between 73.5% and 84.6% (mean 78.9%) of local terms, whereas Lucene's performance was between 66.5% and 76.6% (mean 71.9%). Using a cut-off score of 0.46 Maxent always ranked the correct LOINC code first for over 57% of local terms.
conclusionsThis study showed that a rich corpus of local terms mapped to LOINC contains collective knowledge that can help map terms from other institutions. Using freely available software tools, we developed a data-driven automated approach that operates on term descriptions from existing mappings in the corpus. Accurate and efficient automated mapping methods can help to accelerate adoption of vocabulary standards and promote widespread health information exchange.
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