ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
6 citing papers in PubMed.
- Development and evaluation of a 4M taxonomy from nursing home staff text messages using a fine-tuned generative language model.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Mapping Clinical Documents to theAMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Logical Observation Identifiers Names and Codes (LOINCDiagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
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.
Indexed as
Identifiers
33936520PMC8075502What OpenQuestion holds
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.