The Comprehensive Effect of Depression, Anxiety, and Headache on Pain Intensity and Painkiller Use in Patients with Headache Analyzed by Unsupervised Clustering Using Machine Learning
<b>Background/Objectives</b>: Patients with headache experience depression, anxiety, and reduced quality of life, which are individually associated with pain intensity and painkiller use, but their comprehensive combined effect remains unclear. <b>Methods</b>: Comprehensive p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/13/6/1345 |
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| Summary: | <b>Background/Objectives</b>: Patients with headache experience depression, anxiety, and reduced quality of life, which are individually associated with pain intensity and painkiller use, but their comprehensive combined effect remains unclear. <b>Methods</b>: Comprehensive patient groups were formed based on unsupervised clustering using machine learning algorithms, and their associations were analyzed via ordinary least square regression. K-means and t-distributed stochastic neighbor embedding (t-SNE) combined with hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were applied for clustering. <b>Results</b>: A total of 813 patients were subdivided via K-means clustering (2 clusters) and t-SNE + HDBSCAN clustering (4 clusters). In the K-means clustering, Cluster 1 showed significantly lower peak pain intensity (coefficient [95% CI]: −0.7 [−1 to −0.4]) and frequency of painkiller use (−2.3 [−3.4 to −1.3]) compared to Cluster 0. In the t-SNE + HDBSCAN clustering, Clusters 2 and 3 showed higher peak pain intensity (1.1 [0.5–1.7] and 1.6 [1.0–2.2], respectively) and more frequent painkiller use (2.5 [0.4–4.5] and 4.4 [2.2–6.7], respectively) than Cluster 1. <b>Conclusions</b>: The clustering approach successfully generated groups that reflected a comprehensive profile of depression-, anxiety-, and headache-related quality of life. The clusters demonstrated significant differences which can help better characterize patients based on their psychological and functional impact. |
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| ISSN: | 2227-9059 |