Unsupervised analysis of classical biomedical markers: robustness and medical relevance of patient clustering using bioinformatics tools. Academic Article uri icon

abstract

  • Motivation It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. Method Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES). The 1999–2002 survey was used for training, while data from the 2003–2006 survey was tested as a validation set. Twelve combinations of pre- processing and clustering algorithms were applied to the training set. The quality of the …

publication date

  • January 1, 2012