ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium
Similarity-Based Recommendation of New Concepts to a Terminology.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Logical definition-based identification of potential missing concepts in SNOMED CT.BMC medical informatics and decision making · 2023Article
- The use of SNOMED CT, 2013-2020: a literature review.Journal of the American Medical Informatics Association : JAMIA · 2021Review
- A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
- Amplifying Domain Expertise in Clinical Data Pipelines.JMIR medical informatics · 2020Article
- Extended Analysis of Topological-Pattern-Based Ontology Enrichment.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.Journal of biomedical informatics · 2017Article
- Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.JMIR medical informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation.
Indexed as
Identifiers
26958170PMC4765685What 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.