Interpretation of cluster structures in pain‐related phenotype data using explainable artificial intelligence (XAI)
In: European Journal of Pain, Jg. 25 (2020-11-03), S. 442-465
Online
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Zugriff:
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. Methods Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub-symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100-fold cross-validation. Results The importance of the variables of the data set (6 pain-related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub-symbolic classifiers. A generalized post-hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy. Conclusions Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human-understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy.
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Interpretation of cluster structures in pain‐related phenotype data using explainable artificial intelligence (XAI)
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Autor/in / Beteiligte Person: | Lötsch, Jörn ; Malkusch, Sebastian |
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Zeitschrift: | European Journal of Pain, Jg. 25 (2020-11-03), S. 442-465 |
Veröffentlichung: | Wiley, 2020 |
Medientyp: | unknown |
ISSN: | 1532-2149 (print) ; 1090-3801 (print) |
DOI: | 10.1002/ejp.1683 |
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