ArticleJournal of data science : JDS2023
Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases.
Article in Journal of data science : JDS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Networks are ubiquitous in today's world. Community structure is a well-known feature of many empirical networks, and a lot of statistical methods have been developed for community detection. In this paper, we consider the problem of community structure in text networks, which is greatly relevant in medical errors and patient safety databases. We adapt a well-known community extraction method to develop a scalable algorithm for community extraction in large text databases. The application of our method on a real-world patient safety report database demonstrates that the groups generated from community extraction are much more accurate than manual tagging by frontline workers.
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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.