NDLI: Traditional Chinese medicine pharmacovigilance in signal detection: decision tree-based data classification

Content Provider	Springer Nature : BioMed Central
Author	Wei, Jian-Xiang Wang, Jing Zhu, Yun-Xia Sun, Jun Xu, Hou-Ming Li, Ming
Abstract	Background Traditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently. Methods Reports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample (all data) and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them—PRR, MHRA and IC—were chosen to do the experiment. We designed several indicators for performance evaluation such as R (recall ratio), P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators. Results For PRR: R1-R2 = 0.72%, P1-P2 = 0.16% and D = 0.92%; For MHRA: R1-R2 = 0.97%, P1-P2 = 0.20% and D = 1.18%; For IC: R1-R2 = 1.44%, P2-P1 = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are “separation” for PRR, MHRA and IC. Conclusions In order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-018-0599-5.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-018-0599-5
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2018-03-09
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Adverse drug reaction Signal detection Data classification Decision tree
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

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