ArticlePloS one2017
Data Mining Techniques Applied to Hydrogen Lactose Breath Test.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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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.
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Who cites it
3 citing papers in PubMed.
- Article
- Induced volatolomics of pathologies.Nature reviews. Chemistry · 2021Review
- Lactose Intolerance: What Your Breath Can Tell You.Diagnostics (Basel, Switzerland) · 2020Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAnalyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production.
methodsHydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients.
resultsSix different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test.
conclusionsAnalysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.
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