scholarly journals Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Bing Li ◽  
Xue-Zhong Zhou ◽  
Run-Shun Zhang ◽  
Ying-Hui Wang ◽  
Yonghong Peng ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Data ◽  
Multiple Testing ◽  
Large Scale ◽  
Individualized Medicine ◽  
Correlation Method ◽  
Data Sets ◽  
Chi Square ◽  
Positive Correlations

Background. Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data.Methods. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations.Results. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations.Conclusions. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.

scholarly journals Data Mining and Analysis of the Compatibility Law of Traditional Chinese Medicines Based on FP-Growth Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuchun Zhou
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Association Rules ◽  
Large Scale ◽  
Data Sets ◽  
Prescription Data ◽  
Exploratory Research ◽  
Data Set ◽  
Important Processing ◽  
Tcm Theory

The compatibility law of prescriptions is the core link of TCM theory of “theory, method, prescription and medicine,” which is of great significance for guiding clinical practice, new drug development and revealing the scientific connotation of TCM theory, and is also one of the hot spots and difficulties of TCM modernization research. How to efficiently analyze the frequency of drug use, core combination, and association rules between drugs in prescription is a basic core problem in the study of prescription compatibility law. In this paper, a systematic study was made on the compatibility rules of traditional Chinese antiviral classical prescriptions and the mechanism of traditional Chinese medicine molecules. FP-growth algorithm was used to analyze association rules of 961 classical prescriptions collected and to explore the compatibility rules of traditional Chinese antiviral classical prescriptions. In terms of compatibility law of traditional Chinese antiviral prescriptions, this paper studied the compatibility law of traditional Chinese antiviral prescriptions based on the FP-growth algorithm and made exploratory research on the compatibility law information of 961 traditional classical antiviral prescriptions. Firstly, FP tree was constructed based on the classic recipe data set. Then, frequent item set rules were established, and association rules contained in FP tree were extracted. Finally, the frequency and association rules of antiviral TCM prescriptions were analyzed according to dosage forms (decoction, pill, paste, and ingot). The results show that the FP-growth algorithm adopted in this paper has excellent algorithm performance and strong generalization and robustness in the screening and mining of large-scale prescription data sets, which can provide important processing tools and technical methods for the study of the compatibility rule of traditional Chinese medicine prescriptions.

Dynamic Measurement Modeling: Using Nonlinear Growth Models to Estimate Student Learning Capacity

2017 ◽  
Vol 46 (6) ◽  
pp. 284-292 ◽  
Author(s):  
Denis G. Dumas ◽  
Daniel M. McNeish
Keyword(s):  
Multiple Testing ◽  
Large Scale ◽  
Growth Models ◽  
Dynamic Assessment ◽  
The United States ◽  
Dynamic Measurement ◽  
Data Sets ◽  
Data Systems ◽  
Longitudinal Assessment ◽  
Assessment Practice

Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate opportunity to develop various academic skills but can nonetheless do so in the future. One long-standing approach to the partial rectification of this issue is dynamic assessment (DA), a technique that features multiple testing occasions integrated with learning opportunities. However, DA is extremely resource intensive to incorporate into educational assessment practice and cannot be applied to extant large-scale data sets. In this article, the authors describe a recently developed statistical technique, dynamic measurement modeling (DMM), which is capable of estimating quantities associated with DA—including student capacity for learning a particular skill—from existing large-scale longitudinal assessment data, allowing the core concepts of DA to be scaled up for use with secondary data sets such as those collected by Statewide Longitudinal Data Systems in the United States. The authors show that by considering several assessments over time, student capacity can be reliably estimated, and these capacity estimates are much less affected by student race/ethnicity, gender, and socioeconomic status than are single-timepoint assessment scores, thereby improving the consequential validity of measurement.

scholarly journals Efficacy and Safety of Sophora flavescens (Kushen) Based Traditional Chinese Medicine in the Treatment of Ulcerative Colitis: Clinical Evidence and Potential Mechanisms

2020 ◽  
Vol 11 ◽  
Author(s):  
Mingjun Chen ◽  
Yuxuan Ding ◽  
Zhanqi Tong
Keyword(s):  
Ulcerative Colitis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Large Scale ◽  
Remission Rate ◽  
Chinese Herb ◽  
Biomedical Literature ◽  
Network Pharmacology ◽  
Aminosalicylic Acid ◽  
Acid Therapy

Background: Radix Sophorae flavescentis (Kushen), a Chinese herb, is widely used in the treatment of ulcerative colitis (UC) with damp-heat accumulation syndrome (DHAS) according to traditional Chinese medicine (TCM) theory.Objective: The aim of this study was to illuminate the clinical efficacy and potential mechanisms of Kushen-based TCM formulations in the treatment of UC with DHAS.Materials and Methods: A systematic literature search was performed in the PubMed, EMBASE, Chinese Biomedical Literature database, China National Knowledge Infrastructure database, Chongqing VIP Information database, and Wanfang database for articles published between January 2000 and July 2020 on randomized controlled trials (RCTs) that used Kushen-based TCM formulations in the treatment of UC with DHAS. A network pharmacology approach was conducted to detect the potential pathways of Kushen against UC with DHAS.Results: Eight RCTs with a total of 983 subjects were included in the meta-analysis. Compared with the control subjects (5-aminosalicylic acid therapy), those who received Kushen-based TCM formulations for the treatment of UC showed a significantly higher clinical remission rate (RR = 1.20, 95% CI: [1.04, 1.38], p = 0.02) and lower incidence of adverse events (RR = 0.63, 95% CI [0.39, 1.01], p = 0.06). A component-target-pathway network was constructed, indicating five main components (quercetin, luteolin, matrine, formononetin, and phaseolin), three major targets (Interleukin-6, Myc proto-oncogene protein, and G1/S-specific cyclin-D1) and one key potential therapeutic pathway (PI3K-Akt signaling) of Kushen against UC with DHAS.Conclusion: Kushen-based TCM formulations provide good efficacy and possess great potential in the treatment of UC. Large-scale and high-quality clinical trials and experimental verification should be considered for further confirmation of the efficacy of Kushen-based formulations.

scholarly journals Predicting the Drug Safety for Traditional Chinese Medicine through a Comparative Analysis of Withdrawn Drugs Using Pharmacological Network

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mengzhu Xue ◽  
Shoude Zhang ◽  
Chaoqian Cai ◽  
Xiaojuan Yu ◽  
Lei Shan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Natural Products ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Drug Safety ◽  
Large Scale ◽  
Drug Candidate ◽  
Topology Analysis ◽  
Drug Safety Surveillance ◽  
Approved Drugs

As the major issue to limit the use of drugs, drug safety leads to the attrition or failure in clinical trials of drugs. Therefore, it would be more efficient to minimize therapeutic risks if it could be predicted before large-scale clinical trials. Here, we integrated a network topology analysis with cheminformatics measurements on drug information from the DrugBank database to detect the discrepancies between approved drugs and withdrawn drugs and give drug safety indications. Thus, 47 approved drugs were unfolded with higher similarity measurements to withdrawn ones by the same target and confirmed to be already withdrawn or discontinued in certain countries or regions in subsequent investigations. Accordingly, with the 2D chemical fingerprint similarity calculation as a medium, the method was applied to predict pharmacovigilance for natural products from an in-house traditional Chinese medicine (TCM) database. Among them, Silibinin was highlighted for the high similarity to the withdrawn drug Plicamycin although it was regarded as a promising drug candidate with a lower toxicity in existing reports. In summary, the network approach integrated with cheminformatics could provide drug safety indications effectively, especially for compounds with unknown targets or mechanisms like natural products. It would be helpful for drug safety surveillance in all phases of drug development.

Large-Scale Analysis of Genetic and Clinical Patient Data

2018 ◽  
Vol 1 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Marylyn D. Ritchie
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Data Science ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Phenotypic Data ◽  
Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

scholarly journals Application of Genetic Algorithm for Discovery of Core Effective Formulae in TCM Clinical Data

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ming Yang ◽  
Josiah Poon ◽  
Shaomo Wang ◽  
Lijing Jiao ◽  
Simon Poon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Genetic Algorithm ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Data ◽  
Cancer Dataset ◽  
Synergy Index ◽  
Average Confidence ◽  
Group A ◽  
Tcm Theory

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.

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