Evidence map›Paper›PMID 38585497›Full record

ArticleDermatology reports2024

Natural language processing of online support group postings reveals patients' perspectives on strategies for managing psoriasis.

Karan Rajalingam, Phillip M Johansen, Nicole J Levin, Jerry Qi, Oge Marques

Abstract read
In one paragraph

Article in Dermatology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Karan RajalingamCharles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL.
Phillip M JohansenCharles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL.
Nicole J LevinCharles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL.
Jerry QiCharles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL.
Oge MarquesDepartment of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psoriasis is a chronic skin disorder, and patients encounter high physical and psychosocial burdens. Social media forums feature extensive patient-generated comments. We hypothesized that analyzing patient-posted comments using natural language processing would provide insights into patient engagements, sentiments, concerns, and support, which are vital for the holistic management of psoriasis. We collected 32,000 active user comments posted on Reddit. We applied Latent Dirichlet Allocation to categorize posts into popular topics and employed spectral clustering to establish cohesive themes and word representation frequency within these topics. We sorted posts into 29 significant topics of discussion and categorized them into four categories: management (37.48%), emotion (21.57%), presentation (19.79%), and others (3.57%). The frequent posts on management were diet (7.23%), biologics (6.95%), and adverse effects (3.88%). The emotion category comprised negative sentiments (11.02%), encouragement (5.49%), and gratitude (5.06%). The presentation topic included a discussion of scalp (5.69%), flare-timing (3.63%), and arthritis (2.64%). Others comprised differential diagnosis (5.01%), leaky gut (4.12%), and referrals (3.70%). This study identified patients' experiences and perspectives associated with psoriasis, which should be considered to tailor support systems to improve their quality of life.

Indexed as

dermatologymachine learningpatient engagementpsoriasissocial media

Identifiers

PMID38585497
PMCPMC10993655

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.