Interpretation of cluster structures in pain‐related phenotype data using explainable artificial intelligence (XAI)
- 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.
Author: | Jörn LötschORCiDGND, Sebastian MalkuschORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-576604 |
DOI: | https://doi.org/10.1002/ejp.1683 |
ISSN: | 1532-2149 |
Parent Title (English): | European journal of pain |
Publisher: | Wiley-Blackwell |
Place of publication: | Malden, Mass. [u.a.] |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2020/11/03 |
Date of first Publication: | 2020/11/03 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2021/02/18 |
Volume: | 25.2021 |
Issue: | 2 |
Page Number: | 24 |
First Page: | 442 |
Last Page: | 465 |
HeBIS-PPN: | 476951577 |
Institutes: | Medizin / Medizin |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit | |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung 4.0 |