Development and application of the ocular immune-mediated inflammatory diseases ontology enhanced with synonyms from online patient support forum conversation. (August 2021)

• Record Type: Journal Article
• Authors: Pendleton, Samantha C.; Slater, Luke T.; Karwath, Andreas; Gilbert, Rose M.; Davis, Nicola; Pesudovs, Konrad; Liu, Xiaoxuan; Denniston, Alastair K.; Gkoutos, Georgios V.; Braithwaite, Tasanee
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Uveitis -- Ontology -- Inflammation -- Patient voice -- Sentiment; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104542
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Unstructured text created by patients represents a rich, but relatively inaccessible resource for advancing patient-centred care. This study aimed to develop an ontology for ocular immune-mediated inflammatory diseases (OcIMIDo), as a tool to facilitate data extraction and analysis, illustrating its application to online patient support forum data. Methods: We developed OcIMIDo using clinical guidelines, domain expertise, and cross-references to classes from other biomedical ontologies. We developed an approach to add patient-preferred synonyms text-mined from oliviasvision.org online forum, using statistical ranking. We validated the approach with split-sampling and comparison to manual extraction. Using OcIMIDo, we then explored the frequency of OcIMIDo classes and synonyms, and their potential association with natural language sentiment expressed in each online forum post. Findings: OcIMIDo (version 1.2) includes 661 classes, describing anatomy, clinical phenotype, disease activity status, complications, investigations, interventions and functional impacts. It contains 1661 relationships and axioms, 2851 annotations, including 1131 database cross-references, and 187 patient-preferred synonyms. To illustrate OcIMIDo's potential applications, we explored 9031 forum posts, revealing frequent mention of different clinical phenotypes, treatments, and complications. Language sentiment analysis of each post was generally positive (median 0.12, IQR 0.01–0.24). In multivariable logistic regression, the odds of a post expressing negative sentiment were significantly associated with first posts as compared to replies (OR 3.3, 95% CI 2.8 to 3.9, p < 0.001). Conclusion: We report the development and validation of a new ontology for inflammatory eye diseases, which includes patient-preferred synonyms, and can be used to explore unstructured patient or physician-reported text data, with many potential applications. Graphical abstract: Image 1 Highlights: Here we present OcIMIDo, the first ontology of its kind in ophthalmology. We developed the ontology using domain expertise and clinical guidelines. Novel synonym extraction method with tf-idf to capture patient voice. Facilitates analysis of unstructured text relating to ocular inflammatory diseases.