Stakeholder Insights in Alzheimer’s Disease: Natural Language Processing of Social Media Conversations

Article type: Research Article

Authors: Tahami Monfared, Amir Abbas^{a; b; *} | Stern, Yaakov^c | Doogan, Stephen^d | Irizarry, Michael^a | Zhang, Quanwu^a

Affiliations: [a] Eisai, Inc., Nutley, NJ, USA | [b] Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada | [c] Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA | [d] Real Life Sciences, LLC., New York, NY, USA

Correspondence: [*] Correspondence to: Amir Abbas Tahami Monfared, MD, PhD, Eisai, Inc., 200 Metro Blvd, Nutley, NJ 07110, USA. Tel.: +1 551 502 8770; E-mail: [email protected].

Abstract: Background:Social media data may be especially effective for studying diseases associated with high stigma, such as Alzheimer’s disease (AD). Objective:We primarily aimed to identify issues/challenges experienced by patients with AD using natural language processing (NLP) of social media posts. Methods: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. Results: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. Conclusion:NLP of AD stakeholder social media data revealed a broad spectrum of real-world insights regarding patient burden.

Keywords: Alzheimer’s disease, dementia, mild cognitive impairment, natural language processing, online social networking, patient reported outcome measures, social media

DOI: 10.3233/JAD-220422

Journal: Journal of Alzheimer's Disease, vol. 89, no. 2, pp. 695-708, 2022