Evidence map›Paper›PMID 33381207›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2020

Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books.

Qunsheng Zou, Yinyan Wang, Zixin Shu, Kuo Yang, Jingjing Wang, Kezhi Lu, Qiang Zhu, Baoyan Liu, Runshun Zhang, Xuezhong Zhou

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In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Qunsheng ZouBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0003-2604-2682
Yinyan WangBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0003-0178-222X
Zixin ShuBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0001-6078-5645
Kuo YangBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0003-0736-4512
Jingjing WangBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0002-5202-9943
Kezhi LuBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0003-4979-5097
Qiang ZhuBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0002-4677-1244
Baoyan LiuData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.ORCID https://orcid.org/0000-0002-9677-5366
Runshun ZhangGuang'anmen Hospital, China Academy of Chinese Medicine, Beijing 100700, China.ORCID https://orcid.org/0000-0002-7127-8865
Xuezhong ZhouBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.ORCID https://orcid.org/0000-0002-4713-3594

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link). We first calculated node degrees, average path lengths, and clustering coefficients of the book networks and explored the basic topological correlations between them. Then, we compared the similarity of network nodes to assess the specificity of TCM concepts in the network. In order to explore the relationship between TCM concepts, we screened TCM concepts and clustered them. Finally, we selected the binary groups whose weights are greater than 10 in

Identifiers

PMID33381207
PMCPMC7748907

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.