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

Abstract

The normalization of clinical documents is essential for health information management with the enormous amount of clinical documentation generated each year. The LOINC Document Ontology (DO) is a universal clinical document standard in a hierarchical structure. The objective of this study is to investigate the feasibility and generalizability of LOINC DO by mapping from clinical note titles across five institutions to five DO axes. We first developed an annotation framework based on the definition of LOINC DO axes and manually mapped 4,000 titles. Then we introduced a pre-trained deep learning model named Bidirectional Encoder Representations from Transformers (BERT) to enable automatic mapping from titles to LOINC DO axes. The results showed that the BERT-based automatic mapping achieved improved performance compared with the baseline model. By analyzing both manual annotations and predicted results, ambiguities in LOINC DO axes definition were discussed.

Figure 1.

Sample annotations of note types.

Figure 2.

The architecture of fine-tuned BERT used in this study. “Tok” stands for the token inputs to the BERT model. In this example, “Estab” was labelled as “B-Role”, “Pat” was labelled as “I-Role”, “Visit” was recognized as “ToS”, “Level” and “4” are non-entity tokens.

Figure 3.

Word Cloud for terms and phrases that appeared with top frequencies in note titles.