ArticleJournal of biomedical informatics2011
Degree centrality for semantic abstraction summarization of therapeutic studies.
Article in Journal of biomedical informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text summarization in the biomedical domain: a systematic review of recent research.Journal of biomedical informatics · 2014Pooled it
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- Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.Journal of biomedical informatics · 2017Article
- Generating disease-pertinent treatment vocabularies from MEDLINE citations.Journal of biomedical informatics · 2017Article
- Context-driven automatic subgraph creation for literature-based discovery.Journal of biomedical informatics · 2015Article
- Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury.Journal of biomedical semantics · 2015Article
- PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks.PloS one · 2015Article
- Clustering cliques for graph-based summarization of the biomedical research literature.BMC bioinformatics · 2013Article
- Semantic MEDLINE for discovery browsing: using semantic predications and the literature-based discovery paradigm to elucidate a mechanism for the obesity paradox.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
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- Text summarization as a decision support aid.BMC medical informatics and decision making · 2012Article
- Automatically extracting sentences from Medline citations to support clinicians' information needs.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Automatic summarization has been proposed to help manage the results of biomedical information retrieval systems. Semantic MEDLINE, for example, summarizes semantic predications representing assertions in MEDLINE citations. Results are presented as a graph which maintains links to the original citations. Graphs summarizing more than 500 citations are hard to read and navigate, however. We exploit graph theory for focusing these large graphs. The method is based on degree centrality, which measures connectedness in a graph. Four categories of clinical concepts related to treatment of disease were identified and presented as a summary of input text. A baseline was created using term frequency of occurrence. The system was evaluated on summaries for treatment of five diseases compared to a reference standard produced manually by two physicians. The results showed that recall for system results was 72%, precision was 73%, and F-score was 0.72. The system F-score was considerably higher than that for the baseline (0.47).
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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.