Rubio-Escudero Cristina, Valverde-Fernández Justo, Nepomuceno-Chamorro Isabel, Pontes-Balanza Beatriz, Hernández-Mendoza Yoedusvany, Rodríguez-Herrera Alfonso
Department of Computer Languages and Systems, University of Sevilla, Sevilla, Spain.
Hispalense Pediatrics Institute, Sevilla, Spain.
PLoS One. 2017 Jan 26;12(1):e0170385. doi: 10.1371/journal.pone.0170385. eCollection 2017.
Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production.
Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients.
Six 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.
Analysis 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.
运用数据挖掘工具分析一组氢气呼气试验数据。识别氢气产生的新模式。
将氢气呼气试验数据集以及作为数据挖掘技术的k均值聚类应用于一个包含2571名患者的数据集。
在对氢气呼气试验数据进行分析后提取出六种不同模式。我们还展示了整个测试过程中所采集的每个样本的相关性。
使用数据挖掘技术对氢气呼气试验数据集进行分析，已识别出乳糖吸收时氢气产生的新模式。我们可以看到数据挖掘技术应用于临床数据集的潜力。这些结果为未来关于肠道微生物群产生的氢气与其与临床症状的联系的研究提供了有前景的数据。
