ArticleData and information management2018
Design of a generic, open platform for machine learning-assisted indexing and clustering of articles in PubMed, a biomedical bibliographic database.
Article in Data and information management, 2018. 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.
- A Similarity Metric, Rubric and Unified Hierarchy for Biomedical Publication Types and Study Designs.medRxiv : the preprint server for health sciences · 2026Article
- A similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs.Database : the journal of biological databases and curation · 2026Article
- Enhancing automated indexing of publication types and study designs in biomedical literature using full-text features.medRxiv : the preprint server for health sciences · 2025Article
- Publication Type Tagging using Transformer Models and Multi-Label Classification.medRxiv : the preprint server for health sciences · 2025Article
- Publication Type Tagging using Transformer Models and Multi-Label Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.Journal of biomedical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and employ machine learning algorithms. At present, each research group tackles each problem from scratch, and in isolation of other projects, which causes redundancy and great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects, and can serve as a public repository for their outputs. We will initially focus on a specific goal, namely, classifying articles according to Publication Type, and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning based goals and projects, and can be used as a public platform for disseminating the results of NLP tools to end-users as well.
Indexed as
Identifiers
What 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.