Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data.

ABSTRACT: Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medic

SUBMITTER: Lee H 

PROVIDER: S-EPMC9374171 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature