Differential diagnosis of jaw pain using informatics technology

Journal of Oral Rehabilitation
Y NamH-S Kho

Abstract

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 < .001), but less commonly used words such as "noise" (P < .001) and "temporomandibular joint" (P < .001) than patients with genuine TMD. A diagnostic classification tree on the basis of recursive partitioning suggested that 12.0 mm of comfortable mouth op...Continue Reading

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Citations

Oct 30, 2020·Journal of Dental Research·T ShanL Gu
Jan 15, 2021·Journal of Prosthodontic Research·Takahiro KishimotoTetsuo Ichikawa
May 1, 2020·Dental Materials : Official Publication of the Academy of Dental Materials·Jasmin GrischkeSami Haddadin
Apr 22, 2021·Journal of Medical Internet Research·Hyeonhoon LeeJonghyeon Yeo
May 15, 2021·Pain Research & Management : the Journal of the Canadian Pain Society = Journal De La Société Canadienne Pour Le Traitement De La Douleur·Taseef Hasan FarookMohammad Khursheed Alam
Jul 10, 2021·Journal of Dental Research·F Schwendicke, J Krois

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