Eisai, Inc., Nutley, NJ, USA.
Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
J Alzheimers Dis. 2022;89(2):695-708. doi: 10.3233/JAD-220422.
Social media data may be especially effective for studying diseases associated with high stigma, such as Alzheimer's disease (AD).
We primarily aimed to identify issues/challenges experienced by patients with AD using natural language processing (NLP) of social media posts.
We searched 130 public social media sources between January 1998 and December 2021 for AD stakeholder social media posts using NLP to identify issues/challenges experienced by patients with AD. Issues/challenges identified by ≥10% of any AD stakeholder type were described. Illustrative posts were selected for qualitative review. Secondarily, issues/challenges were organized into a conceptual AD identification framework (ADIF) and representation of ADIF categories within clinical instruments was assessed.
We analyzed 1,859,077 social media posts from 30,341 AD stakeholders (21,011 caregivers; 7,440 clinicians; 1,890 patients). The most common issues/challenges were Worry/anxiety (34.2%), Pain (33%), Malaise (28.7%), Confusional state (27.1%), and Falls (23.9%). Patients reported a markedly higher volume of issues/challenges than other stakeholders. Patient posts reflected the broader scope of patient burden, caregiver posts captured both patient and caregiver burden, and clinician posts tended to be targeted. Less than 5% of the high frequency issues/challenges were in the "function and independence" and "social and relational well-being" categories of the ADIF, suggesting these issues/challenges may be difficult to capture. No single clinical instrument covered all ADIF categories; "social and relational well-being" was least represented.
NLP of AD stakeholder social media data revealed a broad spectrum of real-world insights regarding patient burden.
社交媒体数据对于研究与高污名相关的疾病（如阿尔茨海默病，AD）可能特别有效。
我们主要旨在通过社交媒体帖子的自然语言处理（NLP）来识别 AD 患者所经历的问题/挑战。
我们在 1998 年 1 月至 2021 年 12 月期间，通过 NLP 搜索了 130 个公共社交媒体来源，以获取 AD 利益相关者的社交媒体帖子，以识别 AD 患者所经历的问题/挑战。描述了任何 AD 利益相关者类型中≥10%所识别的问题/挑战。选择了说明性帖子进行定性审查。其次，将问题/挑战组织到一个 AD 识别框架（ADIF）中，并评估了 ADIF 类别在临床工具中的表现。
我们分析了来自 30341 名 AD 利益相关者（21011 名护理人员；7440 名临床医生；1890 名患者）的 1859077 条社交媒体帖子。最常见的问题/挑战是担心/焦虑（34.2%）、疼痛（33%）、不适（28.7%）、混乱状态（27.1%）和跌倒（23.9%）。与其他利益相关者相比，患者报告的问题/挑战明显更多。患者帖子反映了患者负担的更广泛范围，护理人员帖子既包含患者也包含护理人员的负担，而临床医生的帖子则倾向于有针对性。高频问题/挑战中不到 5%属于 ADIF 的“功能和独立性”和“社会和关系幸福感”类别，这表明这些问题/挑战可能难以捕捉。没有单一的临床工具涵盖所有 ADIF 类别；“社会和关系幸福感”的代表性最低。
AD 利益相关者社交媒体数据的 NLP 揭示了有关患者负担的广泛真实世界见解。
