Evidence map›Paper›PMID 39773474›Full record

ArticleBMC cancer2025

Detection of differences in physical symptoms between depressed and undepressed patients with breast cancer: a study using K-medoids clustering.

Jianyao Tang, Bingqian Guo, Chuhan Zhong, Jing Chi, Jiaqi Fu, Jie Lai, Yujie Zhang, Zihan Guo, Shisi Deng, Yanni Wu

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Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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5 · Who and what money

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10 authors.

Jianyao TangNanfang Hospital, Southern Medical University, Guangzhou, China.
Bingqian GuoNanfang Hospital, Southern Medical University, Guangzhou, China.
Chuhan ZhongNanfang Hospital, Southern Medical University, Guangzhou, China.
Jing ChiNanfang Hospital, Southern Medical University, Guangzhou, China.
Jiaqi FuNanfang Hospital, Southern Medical University, Guangzhou, China.
Jie LaiNanfang Hospital, Southern Medical University, Guangzhou, China.
Yujie ZhangNanfang Hospital, Southern Medical University, Guangzhou, China.
Zihan GuoNanfang Hospital, Southern Medical University, Guangzhou, China.
Shisi DengNanfang Hospital, Southern Medical University, Guangzhou, China.
Yanni WuNanfang Hospital, Southern Medical University, Guangzhou, China. yanniwuSMU@126.com.

Funding

Nanfang Hospital 2023J005National Natural Science Foundation of China 72304131
6 · The paper itself

Abstract

backgroundTo detect the differences in physical symptoms between depressed and undepressed patients with breast cancer (BC), including common symptoms, co-occurring symptoms, and symptom clusters based on texts derived from social media and expressive writing.

methodsA total of 1830 texts from social media and expressive writing were collected. The Chi-square test was used to compare the frequency of physical symptoms between depressed and undepressed patients with BC. Symptom lexicon of BC and K-medoids Clustering were used for mining physical symptoms and cluster analysis.

resultsThe common physical symptoms reported by texts included general pains (59.38%), fatigue (26.60%), vomiting (24.82%), swelling of limbs (21.69%), difficulty sleeping (21.56%), nausea (16.78%), alopecia (15.14%), loss of appetite (13.78%), dizziness (11.60%), and concentration problems (11.19%). The frequency of difficulty sleeping (depressed 28.40%; undepressed 18.16%; P = 0.002) in depressed patients was higher than undepressed patients with BC. High co-occurrence was observed in both commonly mentioned symptoms and those less commonly mentioned but frequently co-occurring with them. There were 5 symptom clusters identified in depressed patients and 6 symptom clusters in undepressed patients. Pain-related symptom cluster and gastrointestinal symptom cluster were both identified in the depressed and undepressed patients. The novel immune system impairment symptom cluster consisting of bleeding and fever was found in the undepressed patients.

conclusionsThis study found that difficulty sleeping was reported more frequently, and identified difficulty sleeping-pain symptom cluster in depressed patients. The novel immune system impairment symptom cluster in undepressed patients was detected. Healthcare providers can provide targeted care to depressed and undepressed patients based on these differences. These findings demonstrate that social media can provide new perspectives on symptom experiences. The combination of digital tools and traditional clinical tools for symptom management in follow-up has great potential in the future. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Breast NeoplasmsDepressionAdultAgedCluster AnalysisFatigueFemaleHumansMiddle AgedNauseaPainSocial MediaVomitingBreast cancerDepressionK-medoids clusteringPhysical symptomsSocial media

Identifiers

PMID39773474
PMCPMC11708193

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.