使用自然语言处理和社交媒体数据来了解纤维肌痛患者的生活经验
Lucy Bell1, Beth Fordham1, Sehreen Mumtaz2
1White Swan, Blue Fin Building, Fora, 1st Floor, 110 Southwark Street, London SE1 0SU, UK.
Healthcare (Basel, Switzerland)
|January 8, 2025
概括
纤维肌痛患者寻求更好的医疗保健,将压力,焦虑和食物确定为常见的疼痛触发因素. 社交媒体分析揭示了改善治疗和承认患者经验的机会.
科学领域:
- 社交媒体分析.
- 自然语言处理自然语言处理.
- 患者报告的结果.
背景情况:
- 纤维肌痛在治疗和医疗保健提供方面存在重大未满足的需求.
- 有效的,基于证据的纤维肌痛治疗往往缺乏.
- 患者的观点对于理解纤维肌痛的经历至关重要.
研究的目的:
- 分析社交媒体对话,以了解患有纤维肌痛的患者的经历.
- 确定纤维肌痛爆发的触发因素和通常讨论的治疗方法.
- 发现障碍和机会,以提高纤维肌痛患者的医疗保健服务.
主要方法:
- 从2019年5月到2021年4月,对714,000份与纤维肌痛相关的文件进行了回顾性分析.
- 利用社交媒体数据 (Twitter,Reddit) 的定性和定量分析.
- 采用先进的自然语言处理算法进行大规模数据分析.
主要成果:
- 压力,焦虑和某些食物被确定为纤维肌痛爆发的常见触发因素.
- 常见的相关并发症包括关节炎和刺激性肠综合征 (IBS).
- 报告的治疗包括按摩,冥想和针;患者对医疗保健提供者的缺乏认可和不充分的护理感到丧.
结论:
- 一项使用自然语言处理的大规模研究为患者的体验提供了全面的见解.
- 确定了习惯性症状,触发因素和治疗方式.
- 这些发现为解决纤维肌痛医疗保健提供方面的差距提供了基础.
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