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Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.

Xu Zuo¹, Jianfu Li¹, Bo Zhao¹

¹School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|May 3, 2021

Summary

This summary is machine-generated.

This study explored the feasibility of using the Logical Observation Identifiers Names and Codes (LOINC) Document Ontology for clinical document normalization. A deep learning model demonstrated improved automatic mapping of clinical note titles to LOINC Document Ontology axes.

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Area of Science:

Health Informatics
Natural Language Processing
Medical Terminology

Background:

Clinical document normalization is crucial for effective health information management.
The Logical Observation Identifiers Names and Codes (LOINC) Document Ontology (DO) offers a standardized hierarchical structure for clinical documents.

Purpose of the Study:

To assess the feasibility and generalizability of the LOINC DO.
To map clinical note titles from five institutions to LOINC DO axes.

Main Methods:

Developed an annotation framework based on LOINC DO axes definitions.
Manually mapped 4,000 clinical note titles.
Applied a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for automatic mapping.

Main Results:

The BERT-based automatic mapping model outperformed the baseline model.
Analysis revealed performance improvements in mapping clinical note titles to LOINC DO axes.

Conclusions:

The study demonstrates the potential of BERT for automated clinical document mapping.
Identified ambiguities in LOINC DO axes definitions requiring further clarification.