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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2014, Vol. 8 Issue (3) : 337-346     DOI: 10.1007/s11684-014-0349-8
RESEARCH ARTICLE |
Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine
Xuezhong Zhou1,2,*(),Yubing Li1,Yonghong Peng3,Jingqing Hu4,Runshun Zhang5,Liyun He6,Yinghui Wang5,Lijie Jiang6,Shiyan Yan6,Peng Li6,Qi Xie2,Baoyan Liu2,*()
1. School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
2. China Academy of Chinese Medical Sciences, Beijing 100700, China
3. School of Engineering and Informatics, University of Bradford, BD7 1DP, UK
4. Institute of Basic Theory of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
5. Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
6. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
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Abstract  

Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

Keywords personalized medicine      complex network      clinical phenotype network      traditional Chinese medicine     
Corresponding Authors: Xuezhong Zhou   
Online First Date: 13 August 2014    Issue Date: 09 October 2014
URL:  
http://academic.hep.com.cn/fmd/EN/10.1007/s11684-014-0349-8     OR     http://academic.hep.com.cn/fmd/EN/Y2014/V8/I3/337
Fig.1  Symptom similarity distribution of 82 patients detected by three physicians. The distribution in purple color shows the symptom similarities among three physicians on same patients. We get 246 pairs of symptom similarities from 82 patients, in which 191 (77.6%) pairs have nonzero symptom similarities. Furthermore the similarities between the symptoms of different patients, which are captured by one physician, are also calculated. There are totally 9963 symptom pairs, in which 7625 (76.5%) pairs have nonzero symptom similarities.
Patient IDPhysician APhysician BInner symptom similarity between physiciansNumber of encounters of Physician A with symptom similarity higher than the inner cases
P01Physician 1Physician 20.16729
P01Physician 3Physician 20.098119
P02Physician 2Physician 10.213212
P03Physician 1Physician 30.158121
P03Physician 2Physician 10.22616
P04Physician 3Physician 10.098114
P04Physician 2Physician 30.12510
P05Physician 1Physician 30.158119
P05Physician 3Physician 20.144318
P07Physician 1Physician 30.083329
Tab.1  The comparison of inner symptom similarities between different physicians on same patients and the inter symptom similarities between different encounters held by same physicians
Fig.2  The frequency (represented by percentage) of the top 20 herbs of the three physicians prescribed for 33 patients. It shows that the first 20 herbs most frequently used by the three physicians have a highly different distribution.
Fig.3  Herb similarity of encounters on each physicians and the inner herb similarity between three physicians (physicians 1–3) on same patients. The herb similarities between encounters of each three physicians have a mean of 0.41, which is much larger than the mean (i.e., 0.27) of inner herb similarities of the three physicians prescribed for same patients. Furthermore, physician 1 has its most herb similarities in [0.5, 0.7] and its mean similarity is 0.62.
Fig.4  The symptom co-occurrence network generated from 242 insomnia outpatient encounters. The labels in the network represent degree (the number of links) of nodes. The node with highest degree (i.e., 222) is Dysphoria. There are no isolated nodes in the network and all the nodes have degree larger than 1, which means that all the nodes (i.e., symptoms) have neighboring nodes to form a single giant component.
Fig.5  Syndrome network on insomnia with shared symptoms as links. For 82 insomnia patients, we obtained 36 different syndrome types, which constitute a very dense network.
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