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Comput Biol Med. 2017 Aug 1;87:70-76. doi: 10.1016/j.compbiomed.2017.05.023. Epub 2017 May 22.

Exploring syndrome differentiation using non-negative matrix factorization and cluster analysis in patients with atopic dermatitis.

Author information

1
Department of Ophthalmology, Otorhinolaryngology, and Dermatology of Korean Medicine, Kyung Hee University Hospital at Gangdong, Seoul, 05278, Republic of Korea.
2
Acupuncture and Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea; Department of Science in Korean Medicine, Graduate School, Kyung Hee University Korean Medicine Hospital, Seoul, 130-701, Republic of Korea.
3
Department of Biofunctional Medicine & Diagnostics, Kyung Hee University Korean Medicine Hospital, Seoul, 130-701, Republic of Korea.
4
Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul 130-701, Republic of Korea.
5
Department of Ophthalmology, Otorhinolaryngology, and Dermatology of Korean Medicine, Kyung Hee University Hospital at Gangdong, Seoul, 05278, Republic of Korea; Department of Clinical Korean Medicine, Graduate School, Kyung Hee University, Seoul, 130-701, Republic of Korea.
6
Department of Anesthesiology and Pain Medicine, Graduate School, Kyung Hee University, Seoul 130-701, Republic of Korea.
7
Department of Ophthalmology, Otorhinolaryngology, and Dermatology of Korean Medicine, Kyung Hee University Hospital at Gangdong, Seoul, 05278, Republic of Korea; Department of Ophthalmology, Otorhinolaryngology and Dermatology of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea. Electronic address: inhwajun@khnmc.or.kr.

Abstract

Syndrome differentiation (SD) results in a diagnostic conclusion based on a cluster of concurrent symptoms and signs, including pulse form and tongue color. In Korea, there is a strong interest in the standardization of Traditional Medicine (TM). In order to standardize TM treatment, standardization of SD should be given priority. The aim of this study was to explore the SD, or symptom clusters, of patients with atopic dermatitis (AD) using non-negative factorization methods and k-means clustering analysis. We screened 80 patients and enrolled 73 eligible patients. One TM dermatologist evaluated the symptoms/signs using an existing clinical dataset from patients with AD. This dataset was designed to collect 15 dermatologic and 18 systemic symptoms/signs associated with AD. Non-negative matrix factorization was used to decompose the original data into a matrix with three features and a weight matrix. The point of intersection of the three coordinates from each patient was placed in three-dimensional space. With five clusters, the silhouette score reached 0.484, and this was the best silhouette score obtained from two to nine clusters. Patients were clustered according to the varying severity of concurrent symptoms/signs. Through the distribution of the null hypothesis generated by 10,000 permutation tests, we found significant cluster-specific symptoms/signs from the confidence intervals in the upper and lower 2.5% of the distribution. Patients in each cluster showed differences in symptoms/signs and severity. In a clinical situation, SD and treatment are based on the practitioners' observations and clinical experience. SD, identified through informatics, can contribute to development of standardized, objective, and consistent SD for each disease.

KEYWORDS:

Cluster analysis; Dermatitis; Syndrome differentiation; atopic; k-means cluster analysis; non-negative matrix factorization

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