# Computational LOINC to Support Biomedical Research at Scale

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $312,821

## Abstract

A core requirement for modern data science is the annotation of data and datasets to support linkage,
indirect reference, and reasoning across domain specific knowledgebases. Clinical laboratory data must be
annotated with standard reference concepts to seamlessly play its part in data-science analytics. For over 25
years, the Logical Observation Identifiers Names and Codes (LOINC®) terminology standard from the
Regenstrief Institute has played the role of trusted identifiers for many clinical observations. LOINC codes are
logically composed from constituent Parts to describe unique concepts with sufficient detail to discriminate
specific labs and clinical findings. However, data science ultimately seeks to apply computational reasoning
and inferencing across data collections and public datasets. Static annotations, while establishing unique
identities for biomedical concepts, do not contribute to the goals of reasoning and inference absent asserted
relationships between and among a) the concepts within a specific terminology such as LOINC, and ideally b)
concepts in related terminologies and ontologies. The core purpose of this proposal is to engineer LOINC
content so that datasets that are annotated with LOINC elements (codes and concepts) will facilitate data
science analytics. This will be achieved through OWL rendering, linkage to well-formed external ontologies,
demonstrating applications that leverage the logical associations, and engaging the LOINC and data science
communities to prioritize and validate these efforts. We will restructure LOINC components, terms, and codes
into an Ontology Web Language (OWL) rendering to support reasoning. This will include the formalization of
LOINC groups and potential related aggregations under “uber codes” (e.g. all blood glucoses). We will link
LOINC Components Parts to external, unencumbered ontologies such as Chemical Entities of Biological
Interest (ChEBI). These linkages can inform the hierarchy and relationships asserted in the OWL structure.
We will demonstrate the application of OWL and related hierarchical reasoning services to allow lumping,
splitting and linking of clinical data that is directly or indirectly anchored in LOINC. Using FHIR examples,
provide examples and code libraries that allow observations to be queried and aggregated (e.g. all blood
glucoses). Reasoning LOINC will be distributed as an open-access resource, in harmony with the OBO
community and related biomedical terminology and classification resources. We will leverage existing groups
and organizations such as LOINC Users group, CD2H, and ACT, to solicit use cases and dynamically evaluate
ontology development and priorities.

## Key facts

- **NIH application ID:** 10837892
- **Project number:** 5R01LM013493-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CHRISTOPHER G CHUTE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $312,821
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10837892

## Citation

> US National Institutes of Health, RePORTER application 10837892, Computational LOINC to Support Biomedical Research at Scale (5R01LM013493-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10837892. Licensed CC0.

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