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Prognostic subgroup...
Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework
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- Zhao, Xiang, PhD, 1987- (author)
- Örebro universitet,Institutionen för beteende-, social- och rättsvetenskap
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- Dannenberg, Katharina, 1991- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper
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- Repsilber, Dirk, 1971- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper
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- Gerdle, Björn (author)
- Department of Health, Medicine and Caring Sciences, Pain and Rehabilitation Centre, Linköping University, Linköping, Sweden
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- Molander, Peter (author)
- Department of Health, Medicine and Caring Sciences, Pain and Rehabilitation Centre, Linköping University, Linköping, Sweden; Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
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- Hesser, Hugo, 1982- (author)
- Örebro universitet,Institutionen för beteende-, social- och rättsvetenskap,Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
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(creator_code:org_t)
- Nature Publishing Group, 2024
- 2024
- English.
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In: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 14:1
- Related links:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Questionnaire data collected at pre-treatment and 1-year follow up from 11,995 patients from the Swedish Quality Registry for Pain Rehabilitation were used. Indicators measuring pain characteristics, psychological aspects, and social functioning and general health status were used to form subgroups, and pain interference at follow-up was used for the selection and the performance evaluation of models. A nested cross-validation procedure was used for determining the number of classes (inner cross-validation) and the prediction accuracy of the selected model among unseen cases (outer cross-validation). A four-class solution was identified as the optimal model. Identified subgroups were separable on indicators, predictive of long-term outcomes, and related to background characteristics. Results are discussed in relation to previous clustering attempts of patients with diverse chronic pain conditions. Our analytical approach, as the first to combine mixture modeling with supervised, targeted learning, provides a promising framework that can be further extended and optimized for improving accurate prognosis in pain treatment and identifying clinically meaningful subgroups among chronic pain patients.
Subject headings
- SAMHÄLLSVETENSKAP -- Psykologi -- Tillämpad psykologi (hsv//swe)
- SOCIAL SCIENCES -- Psychology -- Applied Psychology (hsv//eng)
Keyword
- Latent variable mixture modeling
- Machine learning
- Pain classification
- Pain prognosis
Publication and Content Type
- ref (subject category)
- art (subject category)
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