Screening for Superficial Oral Mucosal Lesions in Sjögren’s Disease Using Natural Language Processing (NLP) Approaches

Background/Objectives: Superficial oral mucosal (SOM) lesions are prevalent among patients with Sjögren’s disease (SjD) due to mucosal dryness. Given the limited evidence on screening and referral for SOMs, and the presence of relevant information only in dental clinical notes, a natural language processing (NLP) pipeline was developed to screen for SOMs among SjD patients. This retrospective study analyzed dental clinical notes from 180 linked electronic dental and health records, including both with and without a diagnosis of SjD. Materials and Methods: An annotation schema with four classes (SOMs, signs and symptoms of dry mouth, treatment for xerostomia, referral to specialists) was inductively created using the Extensible Human Oracle Suite of Tools (eHOST) to manually annotate clinical notes. Relevant keyterms were retrieved using a rule-based approach with Python’s Natural Language Toolkit (NLTK). SjD and control groups were compared using Fisher’s Exact tests. Four annotators reviewed ninety-three records. Results: SjD patients (mean age 54.8 ± 11.7 years) had fewer total visits across 15 years but had more dental visits per year (10.2 ± 13.3) than controls. SjD patients were more likely to have oral candidiasis (p = 0.041), exhibit signs and symptoms of dry mouth (p = 0.004), receive treatments for xerostomia (p < 0.001), be treated with cholinergic agonists (p = 0.005), and be referred to a specialist (p = 0.046), but findings were not significant for all SOMs. Additionally, SjD patients had a higher proportion of sialadenitis (p = 0.045), rheumatoid arthritis (p = 0.001), systemic lupus erythematosus (p < 0.001), myalgia/myositis/fibromyalgia (p = 0.010), and anxiety/nervousness (p = 0.004). Conclusions: These findings encourage the feasibility of using text mining from dental clinical notes for screening and management of oral conditions.