ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Automatic MeSH Indexing: Revisiting the Subheading Attachment Problem.
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 5 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
5 citing papers in PubMed.
- Automated indexing in MEDLINE and the Medical Text Indexer (MTI), 2000-2025: a scoping review.Journal of the Medical Library Association : JMLA · 2026Article
- Goals and strategies for the indexing of publication types and study designs.Database : the journal of biological databases and curation · 2026Article
- Sometimes the apple does fall far from the tree: a case study on automatic indexing precision errors in PubMed.Journal of the Medical Library Association : JMLA · 2025Article
- Algorithmic indexing in MEDLINE frequently overlooks important concepts and may compromise literature search results.Journal of the Medical Library Association : JMLA · 2025Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This year less than 200 National Library of Medicine indexers expect to index 1 million articles, and this would not be possible without the assistance of the Medical Text Indexer (MTI) system. MTI is an automated indexing system that provides MeSH main heading/subheading pair recommendations to assist indexers with their heavy workload. Over the years, a lot of research effort has focused on improving main heading prediction performance, but automated fine-grained indexing with main heading/subheading pairs has received much less attention. This work revisits the subheading attachment problem, and demonstrates very significant performance improvements using modern Convolutional Neural Network classifiers. The best performing method is shown to outperform the current MTI implementation with a 3.7% absolute improvement in precision, and a 27.6% absolute improvement in recall. We also conducted a manual review of false positive predictions, and 70% were found to be acceptable indexing.
Indexed as
Identifiers
33936479PMC8075546What 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.