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

Wei, Jianxiang; Wang, Jing; Zhu, Yunxia; Sun, Jun; Xu, Hanmei; Li, Ming

doi:10.1186/s12911-018-0599-5

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…e overall research idea is shown in Figure 4. Wei et al [64] selected the reports from the Chinese spontaneous report database from 2010 to 2011 and used a decision tree to calculate the classification of adverse drug reactions (ADR) signals. Tao Zheng et al [65] adopted a decision tree algorithm to construct a basic data framework.…”

Section: Gain Ratio (A S) � Gain(s A) Split Information(s A)mentioning

confidence: 99%

Different Data Mining Approaches Based Medical Text Data

Xiao

Jing

et al. 2021

Journal of Healthcare Engineering

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The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

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Section: Gain Ratio (A S) � Gain(s A) Split Information(s A)mentioning

confidence: 99%

Different Data Mining Approaches Based Medical Text Data

Xiao

Jing

et al. 2021

Journal of Healthcare Engineering

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“…We vertify our proposed model TPGen on a real-world dataset T LD, collected from lung cancer patients in clinic, and on a public dataset CKCEST [2] [8]. Our dataset consists of 10000+ TCM prescriptions targeted to lung cancer provided by the cooperative hospital.…”

Section: Datasetsmentioning

confidence: 99%

“…Our dataset consists of 10000+ TCM prescriptions targeted to lung cancer provided by the cooperative hospital. The number of the total sample is 10052, in which the dimension of input data, i.e., the symptom, is 189 and [2] https://github.com/yao8839836/PTM the number of the output labels, i.e., herbs, is 357. The statistics of the TCM datasets is summarized in Tab.…”

Section: Datasetsmentioning

confidence: 99%

“…Traditional Chinese Medicine (TCM) is an important part of the Chinese nation's outstanding culture, and it has a long history of development. TCM has received increasing attention as a complementary approach or alternative to modern medicine for its poly-pharmacological effects [1,2]. TCM prescriptions including the lung-cleaning and toxicity-excluding (LCTE) soup have played an important role in treating the ongoing COVID-19 in China [3,4].…”

Section: Introductionmentioning

confidence: 99%

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TPGen: Prescription Generation Using Knowledge-guided Translator

Ruan¹,

Hua²,

Wu³

et al. 2021

Preprint

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Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis

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“…Few studies in China had attempted to use safety signalling tools to monitor THM products safety signal and are still refining their methods and databases. 9,10 There are no local studies in Malaysia investigating the trend of adulterated THM products and the potential use of safety signalling tools for THM products. Therefore, this present study aims to identify the trend of seized adulterated THM products in Malaysia and to assess the safety signalling of ARs associated with THM products.…”

Section: Introductionmentioning

confidence: 99%

Adulterated Traditional-Herbal Medicinal Products and Its Safety Signals in Malaysia

Ariffin¹,

Wahab²,

Wahab³

2021

DHPS

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Background: Usage of traditional-herbal medicines (THM) for various illnesses has been increased around the world, so does the adulteration of these products with hazardous compounds. There are limited Malaysian data that have been published on the characteristics trend and adverse events associated with adulterated THM products. Aim: This study described characteristics of adulterated THM products in Malaysia and aimed to quantify THM products' safety signals of adverse reactions (ARs). Methods: THM products that were seized by Pharmacy Enforcement Division between 2008 and 2014 were extracted and analysed for 59,440 THM products. Of these, only 6452 THM products with complete information were included in the final analyses. Safety signalling tools were used to measure AR signals from AR reports obtained from the National Pharmaceutical Regulatory Agency Adverse Drug Reaction Database. Results: More than half of adulterated THM products originated from countries outside of Malaysia, with the majority were from Indonesia. The most common claimed indication of adulterated THM products was for pain and fever relief, while steroids were the most common adulterant. AR signals were generated for cough and cold products for respiratory and thoracic disorders, weight-loss products for cardiac disorders, and women's health products for reproductive and breast disorders. Conclusion:Health authorities from various fields can work collaboratively by implementing strategic actions that include the use of safety signalling tools to curb the increasing number of adulterated THM products in the Malaysian market.

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

Cited by 13 publications

References 14 publications

Different Data Mining Approaches Based Medical Text Data

Different Data Mining Approaches Based Medical Text Data

TPGen: Prescription Generation Using Knowledge-guided Translator

Adulterated Traditional-Herbal Medicinal Products and Its Safety Signals in Malaysia

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