Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients

Conclusions Our method groups data removing the effect of counfounding factors without making any assumptions about the form of the influence of these factors on the other features. We identified clusters of MS patients that have clinically recognizable differences. Because patients more likely to progress are found using this approach, our results have the potential to aid physicians in tailoring treatment decisions for MS patients.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research