Differential diagnosis of jaw pain using informatics technology

Summary This study aimed to deduce evidence‐based clinical clues that differentiate temporomandibular disorders (TMD)‐mimicking conditions from genuine TMD by text mining using natural language processing (NLP) and recursive partitioning. We compared the medical records of 29 patients diagnosed with TMD‐mimicking conditions and 290 patients diagnosed with genuine TMD. Chief complaints and medical histories were preprocessed via NLP to compare the frequency of word usage. In addition, recursive partitioning was used to deduce the optimal size of mouth opening, which could differentiate TMD‐mimicking from genuine TMD groups. The prevalence of TMD‐mimicking conditions was more evenly distributed across all age groups and showed a nearly equal gender ratio, which was significantly different from genuine TMD. TMD‐mimicking conditions were caused by inflammation, infection, hereditary disease and neoplasm. Patients with TMD‐mimicking conditions frequently used “mouth opening limitation” (P