分析社交媒体上慢性病患者与创伤后应激障碍的关联，发现可借算法识别高风险人群。
A Review: PTSD in Pre-Existing Medical Condition on Social Media
- 用NLP和机器学习分析社交平台文本，识别共病群体中的潜在PTSD
- 识别准确率达74%至90%，为早期干预提供数据支持
- 适合心理健康研究者、数字医疗从业者关注
创伤后应激障碍（PTSD）是一种复杂的心理健康问题，尤其对已有慢性疾病者构成挑战。本文系统回顾2008至2024年文献，探讨癌症、心脏病、自身免疫性疾病等慢性病患者在社交媒体（如X、Facebook）上表达的PTSD表现与应对方式。研究发现，通过自然语言处理（NLP）与机器学习（ML）技术，可从社交平台数据中有效识别该群体的潜在PTSD病例，准确率介于74%至90%之间。在线支持社区在塑造应对策略和促进早期干预方面发挥重要作用。论文强调应在PTSD研究与治疗中纳入既往病史因素，凸显社交媒体作为脆弱群体监测与支持工具的潜力，并提出未来研究方向与临床应用建议。
原文摘要 · Abstract (English)
Post-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition, particularly challenging for individuals with pre-existing medical conditions. This review critically examines the intersection of PTSD and chronic illnesses as expressed on social media platforms. By systematically analyzing literature from 2008 to 2024, the study explores how PTSD manifests and is managed in individuals with chronic conditions such as cancer, heart disease, and autoimmune disorders, with a focus on online expressions on platforms like X (formally known as Twitter) and Facebook. Findings demonstrate that social media data offers valuable insights into the unique challenges faced by individuals with both PTSD and chronic illnesses. Specifically, natural language processing (NLP) and machine learning (ML) techniques can identify potential PTSD cases among these populations, achieving accuracy rates between 74% and 90%. Furthermore, the role of online support communities in shaping coping strategies and facilitating early interventions is highlighted. This review underscores the necessity of incorporating considerations of pre-existing medical conditions in PTSD research and treatment, emphasizing social media's potential as a monitoring and support tool for vulnerable groups. Future research directions and clinical implications are also discussed, with an emphasis on developing targeted interventions.
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