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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/13/6/1345 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849435404526157824 |
|---|---|
| author | Jong-Ho Kim Minha Ahn Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon |
| author_facet | Jong-Ho Kim Minha Ahn Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon |
| author_sort | Jong-Ho Kim |
| collection | DOAJ |
| description | <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. |
| format | Article |
| id | doaj-art-5f95ca1df0e04a1fb37691a73dfd521d |
| institution | Kabale University |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-5f95ca1df0e04a1fb37691a73dfd521d2025-08-20T03:26:17ZengMDPI AGBiomedicines2227-90592025-05-01136134510.3390/biomedicines13061345The Comprehensive Effect of Depression, Anxiety, and Headache on Pain Intensity and Painkiller Use in Patients with Headache Analyzed by Unsupervised Clustering Using Machine LearningJong-Ho Kim0Minha Ahn1Jong-Hee Sohn2Sung-Mi Hwang3Jae-Jun Lee4Young-Suk Kwon5Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaInstitute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaInstitute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of KoreaDepartment of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea<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.https://www.mdpi.com/2227-9059/13/6/1345headacheartificial intelligenceclusteringdepressionanxietyquality of life |
| spellingShingle | Jong-Ho Kim Minha Ahn Jong-Hee Sohn Sung-Mi Hwang Jae-Jun Lee Young-Suk Kwon 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 Biomedicines headache artificial intelligence clustering depression anxiety quality of life |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | comprehensive effect of depression anxiety and headache on pain intensity and painkiller use in patients with headache analyzed by unsupervised clustering using machine learning |
| topic | headache artificial intelligence clustering depression anxiety quality of life |
| url | https://www.mdpi.com/2227-9059/13/6/1345 |
| work_keys_str_mv | AT jonghokim thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT minhaahn thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT jongheesohn thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT sungmihwang thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT jaejunlee thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT youngsukkwon thecomprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT jonghokim comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT minhaahn comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT jongheesohn comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT sungmihwang comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT jaejunlee comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning AT youngsukkwon comprehensiveeffectofdepressionanxietyandheadacheonpainintensityandpainkilleruseinpatientswithheadacheanalyzedbyunsupervisedclusteringusingmachinelearning |