Volume 2, Issue 4, October 2015, Pages 270–283
Open Access
Volume 2, Issue 4, October 2015, Pages 270–283
Open Access
Detection of consensuses and treatment principles of diabetic nephropathy in traditional Chinese medicine: A new approach
Abstract
Objective
To
propose and test a new approach based on community detection in the
field of social computing for uncovering consensuses and treatment
principles in traditional Chinese medicine (TCM).
To
propose and test a new approach based on community detection in the
field of social computing for uncovering consensuses and treatment
principles in traditional Chinese medicine (TCM).
Methods
Three
Chinese databases (CNKI, VIP, and Wan Fang Data) were searched for
published articles on TCM treatment of diabetic nephropathy (DN) from
their inception until September 31, 2014. Zheng classification and herb
data were extracted from included articles and used to construct a Zheng
classification and treatment of diabetic nephropathy (DNZCT) network
with nodes denoting Zhengs and herbs and edges denoting corresponding
treating relationships among them. Community detection was applied to
the DNZCT and detected community structures were analyzed.
Three
Chinese databases (CNKI, VIP, and Wan Fang Data) were searched for
published articles on TCM treatment of diabetic nephropathy (DN) from
their inception until September 31, 2014. Zheng classification and herb
data were extracted from included articles and used to construct a Zheng
classification and treatment of diabetic nephropathy (DNZCT) network
with nodes denoting Zhengs and herbs and edges denoting corresponding
treating relationships among them. Community detection was applied to
the DNZCT and detected community structures were analyzed.
Results
A
network of 201 nodes and 743 edges were constructed and six communities
were detected. Nodes clustered in the same community captured the same
semantic topic; different communities had unique characteristics, and
indicated different treatment principles. Large communities usually
represented similar points of view or consensuses on common Zheng
diagnoses and herb prescriptions; small communities might help to
indicate unusual Zhengs and herbs.
A
network of 201 nodes and 743 edges were constructed and six communities
were detected. Nodes clustered in the same community captured the same
semantic topic; different communities had unique characteristics, and
indicated different treatment principles. Large communities usually
represented similar points of view or consensuses on common Zheng
diagnoses and herb prescriptions; small communities might help to
indicate unusual Zhengs and herbs.
Conclusion
The
results suggest that the community detection-based approach is useful
and feasible for uncovering consensuses and treatment principles of DN
treatment in TCM, and could be used to address other similar problems in
TCM.
The
results suggest that the community detection-based approach is useful
and feasible for uncovering consensuses and treatment principles of DN
treatment in TCM, and could be used to address other similar problems in
TCM.
Keywords
- Social computing;
- Community detection;
- Zheng classification and treatment;
- Diabetic nephropathy;
- Traditional Chinese medicine
- Social computing;
- Community detection;
- Zheng classification and treatment;
- Diabetic nephropathy;
- Traditional Chinese medicine
Introduction
In
complex network theory, a social network is a social structure made of
nodes (individuals or organizations) connected by edges to exemplify
various relationships such as friendship, affiliation, or cooperation.
Community detection is one of the fundamental tasks in social network
analysis. It solves problems by studying groups rather than individuals.
Finding a community in a social network is a function of identifying a
group of nodes that interact with each other more frequently than with
nodes outside the group.1
The real-world significance of identifying such communities are, for
example, that friends in the same group share more similar interests and
interact with each other more frequently2; that community analysis has uncovered thematic clusters on the Internet3; and, in biochemical or neural networks, that communities may be functional modules.4
Nowadays, social computing research has gradually shifted from its
traditional research fields such as computer science and engineering to
other fields such as health services and communications.5 He6 et al and Chang7
et al extended social cooperation networks to the field of traditional
Chinese medicine (TCM) based on a theory that different herbs work
together in a complementary manner to treat a disease. Their research
also presented a traditional Chinese herbal prescription formulation
network (TCHPFN).
In China, TCM herbal formulas have been widely used to treat many diseases. Zheng classification and treatment (ZCT, bian zheng lun zhi,
in Chinese) is a unique feature of TCM, and use of TCM herbal formulas
must follow ZCT. Many studies have been conducted to illustrate that
treatment based on Zheng classification can improve specificity and
efficiency in both TCM and Western medicine. 8, 9 and 10 However, Zheng classification depends mostly on the observations, knowledge, and clinical experience of TCM practitioners.11
Zheng diagnosis always varies from practitioners, leading to different
formulas, although with equivalent efficacy. There might be underlying
consensuses and treatment principles among TCM practitioners to guide
their treatments. Therefore, we proposed a community detection-based
approach to uncover the underlying consensuses and treatment principles,
and tested the approach by applying it to the Zheng classification and
treatment of diabetic nephropathy (DNZCT) data. We present the DNZCT
network as a social cooperation network of different Zhengs and herbs,
and analyze the potential communities in the DNZCT.
In
complex network theory, a social network is a social structure made of
nodes (individuals or organizations) connected by edges to exemplify
various relationships such as friendship, affiliation, or cooperation.
Community detection is one of the fundamental tasks in social network
analysis. It solves problems by studying groups rather than individuals.
Finding a community in a social network is a function of identifying a
group of nodes that interact with each other more frequently than with
nodes outside the group.1
The real-world significance of identifying such communities are, for
example, that friends in the same group share more similar interests and
interact with each other more frequently2; that community analysis has uncovered thematic clusters on the Internet3; and, in biochemical or neural networks, that communities may be functional modules.4
Nowadays, social computing research has gradually shifted from its
traditional research fields such as computer science and engineering to
other fields such as health services and communications.5 He6 et al and Chang7
et al extended social cooperation networks to the field of traditional
Chinese medicine (TCM) based on a theory that different herbs work
together in a complementary manner to treat a disease. Their research
also presented a traditional Chinese herbal prescription formulation
network (TCHPFN).
In China, TCM herbal formulas have been widely used to treat many diseases. Zheng classification and treatment (ZCT, bian zheng lun zhi,
in Chinese) is a unique feature of TCM, and use of TCM herbal formulas
must follow ZCT. Many studies have been conducted to illustrate that
treatment based on Zheng classification can improve specificity and
efficiency in both TCM and Western medicine. 8, 9 and 10 However, Zheng classification depends mostly on the observations, knowledge, and clinical experience of TCM practitioners.11
Zheng diagnosis always varies from practitioners, leading to different
formulas, although with equivalent efficacy. There might be underlying
consensuses and treatment principles among TCM practitioners to guide
their treatments. Therefore, we proposed a community detection-based
approach to uncover the underlying consensuses and treatment principles,
and tested the approach by applying it to the Zheng classification and
treatment of diabetic nephropathy (DNZCT) data. We present the DNZCT
network as a social cooperation network of different Zhengs and herbs,
and analyze the potential communities in the DNZCT.
