Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI).
Journal:
European journal of pain (London, England)
Published Date:
Nov 3, 2020
Abstract
BACKGROUND: In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster.