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利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体验。

Using Natural Language Processing and Social Media Data to Understand the Lived Experience of People with Fibromyalgia.

作者信息

Bell Lucy, Fordham Beth, Mumtaz Sehreen, Yaman Reena, Balistreri Lisa, Butendieck Ronald R, Irani Anushka

机构信息

White Swan, Blue Fin Building, Fora, 1st Floor, 110 Southwark Street, London SE1 0SU, UK.

Division of Rheumatology, Mayo Clinic Florida, Jacksonville, FL 32224, USA.

出版信息

Healthcare (Basel). 2024 Dec 11;12(24):2511. doi: 10.3390/healthcare12242511.

DOI:10.3390/healthcare12242511
PMID:39765938
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11728136/
Abstract

BACKGROUND AND OBJECTIVES

Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients' perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery.

METHODS

A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients' experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses.

RESULTS

Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs.

CONCLUSIONS

We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision.

摘要

背景与目的

纤维肌痛在治疗方面存在许多未满足的需求,且缺乏有效且基于证据的医疗服务。我们分析了社交媒体对话,以了解患者对纤维肌痛生活经历的看法、据报道会引发疼痛发作的因素以及正在讨论的治疗方法,识别改善医疗服务的障碍和机会。

方法

一项非干预性回顾性分析,通过714,000份文档获取了关于纤维肌痛患者经历的丰富详细对话,这些文档包括一个纤维肌痛语言标签,时间跨度为2019年5月至2021年4月。通过定性和定量分析对数据进行分析。

结果

在推特和红迪网上发现关于纤维肌痛的对话最多,且对话趋势随时间保持稳定。有许多环境因素和可改变的触发因素,从最常见的压力和焦虑触发因素到各种食物。关节炎和肠易激综合征(IBS)是最常伴发的疾病。纤维肌痛患者报告了广泛的症状,疼痛是主要特征。按摩、冥想和针灸领域是报告最多的治疗方式。通过当前患者对疾病未得到认可、症状被轻视以及护理需求未得到充分满足的不满,确定了医疗服务提供者改善医疗服务的机会。

结论

我们开展了一项全面的大规模研究,强调了先进的自然语言处理算法在现实世界研究设计中的应用。通过广泛概括患者观点,我们概述了习惯性症状、触发因素和治疗方式,为弥补医疗服务差距提供了坚实基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/27f2bdc32b85/healthcare-12-02511-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/483c2068f635/healthcare-12-02511-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/839fd9c27dd4/healthcare-12-02511-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/fe84ffef3a23/healthcare-12-02511-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/333fcd74d427/healthcare-12-02511-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/27f2bdc32b85/healthcare-12-02511-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/483c2068f635/healthcare-12-02511-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/839fd9c27dd4/healthcare-12-02511-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/fe84ffef3a23/healthcare-12-02511-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/333fcd74d427/healthcare-12-02511-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91c4/11728136/27f2bdc32b85/healthcare-12-02511-g005.jpg

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