English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Approximately 2.4 million patients in Japan would benefit from treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment. Methods We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output. Results The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model. Conclusions We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic.

Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests