Comorbidities and emotions - unpacking the sentiments of pediatric patients with multiple long-term conditions through social media feedback: A large language model-driven study

IF 4.9 2区医学 Q1 CLINICAL NEUROLOGY

Journal of affective disorders Pub Date : 2025-06-21 DOI:10.1016/j.jad.2025.119752

Temidayo I. Oluwalade , Hossein Ahmadi , Lin Huo , Richard Sharpe , Shang-Ming Zhou

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引用次数: 0

Abstract

Objectives

The emotional and psychological challenges faced by children with multiple long-term conditions (MLTCs) remain underexplored. This study aimed to analyze sentiments and emotions expressed by this vulnerable population and their caregivers on social media, assess the effects of comorbidities and the COVID-19 pandemic on emotional well-being.

Methods

Narratives from the Care Opinion platform (2008–2023) were analyzed by a model called CoEmoBERT, developed using the large language model, distilroberta-base transformer model. The CoEmoBERT-based sentiment analysis categorized emotions into “Positive”, “Negative”, and “Neutral,” with further refinements into specific emotions such as “Sad,” “Fear”, “Satisfied” etc. through pretraining and transferring process. Comorbidity associations with emotions were analyzed. We further examined the impact of the COVID-19 pandemic on patient sentiments and investigated temporal trends in emotional expressions.

Results

Of 389 narratives, 93.8 % reflected negative sentiments, with “Sad” (60.9 %) and “Fear” (15.4 %) being the most prevalent. Negative emotions were linked to severe comorbidities like asthma, cancer, and chronic pain, highlighting the emotional burden of managing MLTCs. Positive sentiments (5.9 %) were associated with effective communication and exceptional healthcare experiences. The analysis revealed strong associations between certain comorbidity combinations and specific emotional responses, with mental health conditions showing the most diverse range of comorbidities and emotional impacts. The COVID-19 pandemic exacerbated negative sentiments, particularly sadness and disgust.

Conclusion

This study underscores the significant emotional burden on children with MLTCs, emphasizing the need for integrated care approaches to both physical and emotional well-being. These findings can guide the development of patient-centered care for this population.

查看原文本刊更多论文

合并症和情绪——通过社交媒体反馈揭示患有多种长期疾病的儿科患者的情绪：一项大型语言模型驱动的研究。

目的：多重长期疾病（MLTCs）儿童所面临的情绪和心理挑战仍未得到充分探讨。本研究旨在分析弱势群体及其护理人员在社交媒体上表达的情绪和情绪，评估合并症和COVID-19大流行对情绪健康的影响。方法：采用CoEmoBERT模型对来自Care Opinion平台（2008-2023）的叙述进行分析，该模型采用基于蒸馏roberta的大型语言模型开发。基于coemobert的情绪分析将情绪分为“积极”、“消极”和“中性”，并通过预训练和迁移过程进一步细化为“悲伤”、“恐惧”、“满意”等具体情绪。分析共病与情绪的关系。我们进一步研究了COVID-19大流行对患者情绪的影响，并调查了情绪表达的时间趋势。结果：在389篇叙事中，93.8% %反映了负面情绪，其中“悲伤”（60.9% %）和“恐惧”（15.4% %）最为普遍。负面情绪与严重的合并症有关，如哮喘、癌症和慢性疼痛，突出了管理MLTCs的情绪负担。积极的情绪（5.9 %）与有效的沟通和特殊的医疗体验有关。分析显示，某些合并症组合与特定情绪反应之间存在强烈关联，精神健康状况显示出最多样化的合并症和情绪影响。COVID-19大流行加剧了负面情绪，特别是悲伤和厌恶。结论：本研究强调了MLTCs儿童的重大情感负担，强调了对身体和情感健康的综合护理方法的必要性。这些发现可以指导以患者为中心的护理的发展。

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来源期刊

Journal of affective disorders 医学-精神病学

CiteScore

10.90

自引率

6.10%

发文量

1319

审稿时长

9.3 weeks

期刊介绍： The Journal of Affective Disorders publishes papers concerned with affective disorders in the widest sense: depression, mania, mood spectrum, emotions and personality, anxiety and stress. It is interdisciplinary and aims to bring together different approaches for a diverse readership. Top quality papers will be accepted dealing with any aspect of affective disorders, including neuroimaging, cognitive neurosciences, genetics, molecular biology, experimental and clinical neurosciences, pharmacology, neuroimmunoendocrinology, intervention and treatment trials.