scholarly journals Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Shilun Yang ◽  
Yanjia Shen ◽  
Wendan Lu ◽  
Yinglin Yang ◽  
Haigang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Cross Validation ◽  
Bayesian Models ◽  
Machine Learning Algorithms ◽  
Therapeutic Effects ◽  
Test Set ◽  
Screening Experiments ◽  
Fold Cross Validation

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ratchadaporn Kanawong ◽  
Tayo Obafemi-Ajayi ◽  
Tao Ma ◽  
Dong Xu ◽  
Shao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Color Space ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Control Group

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

scholarly journals Identifying the Main Risk Factors for CVD Prediction Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Luis Rolando Guarneros-Nolasco ◽  
Nancy Aracely Cruz-Ramos ◽  
Giner Alor-Hernández ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Luis Sánchez-Cervantes
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Algorithm Performance ◽  
Body Regions ◽  
Risks Factors ◽  
Fold Cross Validation

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

scholarly journals Research of Machine Learning algorithms using K-fold cross validation

2019 ◽  
Vol 8 (6S) ◽  
pp. 215-218
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Validation Data ◽  
Machine Learning Classification ◽  
Fold Cross Validation

In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on UCI breast cancer dataset with preprocessing. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python.

scholarly journals Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study

2019 ◽  
Vol 9 (16) ◽  
pp. 3322 ◽  
Author(s):  
Stephen Dankwa ◽  
Wenfeng Zheng
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Grid Search ◽  
Baseline Model ◽  
Vector Machines ◽  
Ann Models ◽  
Fold Cross Validation

Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine learning algorithms, which were, the Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Network (ANN) to predict kyphosis disease based on a biomedical data. At the initial stage of the experiments, we performed 5- and 10-Fold Cross-Validation using Logistic Regression as a baseline model to compare with our ML models without performing grid search. We then evaluated the models and compared their performances based on 5- and 10-Fold Cross-Validation after running grid search algorithms on the ML models. Among the Support Vector Machines, we experimented with the three kernels (Linear, Radial Basis Function (RBF), Polynomial). We observed overall accuracies of the models between 79%–85%, and 77%–86% based on the 5- and 10-Fold Cross-Validation, after running grid search respectively. Based on the 5- and 10-Fold Cross-Validation as evaluation metrics, the RF, SVM-RBF, and ANN models achieved accuracies more than 80%. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN model outperformed all the other ML models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation. We proposed that RF, SVM-RBF and ANN models should be used to detect and predict kyphosis disease after a patient had undergone surgery or operation. We suggest that machine learning should be adopted and used as an essential and critical tool across the maximum spectrum of answering biomedical questions.

scholarly journals P031: Using machine learning algorithms for predicting future performance of emergency medicine residents

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S88
Author(s):  
A. Ariaeinejad ◽  
R. Patel ◽  
T.M. Chan ◽  
R. Samavi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Medical Education ◽  
Cross Validation ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Resident Performance ◽  
Competency Based ◽  
Fold Cross Validation

Introduction: Background: Medical education is transitioning from a time-based system to a competency-based framework. In the age of Competency-Based Medical Education, however, there is a drastically increased amount of data that needs to be interpreted. With this data, however, comes an opportunity to develop predictive analytics. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine learning has been successfully used in other fields to create predictive models. Objective: This study evaluates the application of neural network as a machine learning algorithm in learning from historical data in emergency residency program and predicting future resident performance. Methods: We analyzed performance data for 16 residents (PGY1-5) who were assessed at end of each shift. Performance was graded in each of the CanMEDS Roles with scores from 1 to 7 by different attending physicians who observed residents during the shift. We transformed sequences of scores for each resident to a fixed set of features and combined all of them in one dataset. We considered scores under 6 as “At Risk Resident” and scores 6 or more as “Competent Resident”, and then we separated the dataset into training and testing sets using K-Fold cross validation and trained an artificial Neural Network in order to make decision about the future situation of residents in a specific CanMEDS Role and general performance. Results: We used 5-fold cross validation to evaluate the model, one round of cross-validation involves partitioning the whole data into complementary subsets, performing the training phase on the training set, and validating the analysis on the testing set. To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. Results of cross validation show that accuracy of model was 72%, sensitivity was 81% and specificity was 43%. Conclusion: Machine learning algorithms such (as Neural Network) have the ability to predict future resident performance on a global level and within specific domains (i.e. CanMEDS roles). Used appropriately, such information may be a valuable for monitoring resident progress.

scholarly journals Therapeutic Effects of Traditional Chinese Medicine on Spinal Cord Injury: A Promising Supplementary Treatment in Future

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qian Zhang ◽  
Hao Yang ◽  
Jing An ◽  
Rui Zhang ◽  
Bo Chen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Epigallocatechin Gallate ◽  
Therapeutic Effects ◽  
Cord Injury ◽  
Supplementary Treatment ◽  
Natural Drugs ◽  
Doctoral Dissertations

Objective. Spinal cord injury (SCI) is a devastating neurological disorder caused by trauma. Pathophysiological events occurring after SCI include acute, subacute, and chronic phases, while complex mechanisms are comprised. As an abundant source of natural drugs, Traditional Chinese Medicine (TCM) attracts much attention in SCI treatment recently. Hence, this review provides an overview of pathophysiology of SCI and TCM application in its therapy.Methods. Information was collected from articles published in peer-reviewed journals via electronic search (PubMed, SciFinder, Google Scholar, Web of Science, and CNKI), as well as from master’s dissertations, doctoral dissertations, and Chinese Pharmacopoeia.Results. Both active ingredients and herbs could exert prevention and treatment against SCI, which is linked to antioxidant, anti-inflammatory, neuroprotective, or antiapoptosis effects. The detailed information of six active natural ingredients (i.e., curcumin, resveratrol, epigallocatechin gallate, ligustrazine, quercitrin, and puerarin) and five commonly used herbs (i.e., Danshen, Ginkgo, Ginseng, Notoginseng, and Astragali Radix) was elucidated and summarized.Conclusions. As an important supplementary treatment, TCM may provide benefits in repair of injured spinal cord. With a general consensus that future clinical approaches will be diversified and a combination of multiple strategies, TCM is likely to attract greater attention in SCI treatment.

scholarly journals Clinical Therapeutic Effects of Aspirin in Combination with Fufang Danshen Diwan, a Traditional Chinese Medicine Formula, on Coronary Heart Disease: A Systematic Review and Meta-Analysis

2016 ◽  
Vol 39 (5) ◽  
pp. 1955-1963 ◽  
Author(s):  
Jianchun Huang ◽  
Xiaojun Tang ◽  
Fangxing Ye ◽  
Junhui He ◽  
Xiaolong Kong
Keyword(s):  
Systematic Review ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Randomized Clinical Trials ◽  
Meta Analysis ◽  
Aspirin Therapy ◽  
Therapeutic Effects ◽  
Chinese Medicine Formula

Background/Aims: Coronary heart disease is characterized by vascular stenosis or occlusion resulting in myocardial ischemia, hypoxia and necrosis. In China, the combination of aspirin and Fufang Danshen Diwan (FDD), a traditional Chinese medicine formula, has been suggested in the treatment of coronary heart disease. There have been several studies comparing the effectiveness of aspirin alone and in combination with FDD to treat coronary artery disease; however, it remains unclear whether combined aspirin therapy is superior. This study was thus designed to clarify this issue through a systematic review and meta-analysis. Methods: Databases including PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) database, Wanfang Data and VIP Information were searched. Papers were reviewed systematically by two researchers and analyzed using Cochrane software Revman 5.1. Results: Fourteen randomized controlled trials enrolling 1367 subjects were included. Meta-analyses revealed that aspirin in combination with FDD was significantly more effective at alleviating angina pectoris and improving electrocardiogram (ECG) results relative to aspirin therapy alone, reflected by the summary effects for the clinical markedly effective (OR = 2.45; 95% CI 1.95-3.08) and the total effective (OR = 3.92; 95% CI 2.87-5.36) rates. In addition, combined aspirin and FDD was significantly more efficacious than aspirin monotherapy at improving blood lipid levels, as indicated by the following outcomes: 1) reduction of TC level (SMD −1.12; 95% CI −1.49 to −0.76); 2) reduction of TG level (SMD −0.94; 95% CI −1.15 to -0.74); 3) reduction of LDL level (SMD -0.68; 95% CI -0.88 to -0.48); and 4) improvement of HDL level (SMD 0.52; 95% CI 0.04 to 0.99 ). No serious adverse events were reported in any of the included trials. Conclusion: The present meta-analysis demonstrated that aspirin in combination with FDD was more effective than aspirin alone for treating coronary heart disease. More full-scale randomized clinical trials with reliable designs are recommended to further evaluate the clinical benefits and long-term effectiveness of FDD for the treatment of coronary heart disease.

scholarly journals Molecular Imaging in Traditional Chinese Medicine Therapy for Neurological Diseases

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zefeng Wang ◽  
Haitong Wan ◽  
Jinhui Li ◽  
Hong Zhang ◽  
Mei Tian
Keyword(s):  
Chinese Medicine ◽  
Molecular Imaging ◽  
Traditional Chinese Medicine ◽  
Neurological Disorders ◽  
Neurological Diseases ◽  
Chinese Herb ◽  
Public Health Care ◽  
Therapeutic Effects ◽  
Cord Injury

With the speeding tendency of aging society, human neurological disorders have posed an ever increasing threat to public health care. Human neurological diseases include ischemic brain injury, Alzheimer’s disease, Parkinson’s disease, and spinal cord injury, which are induced by impairment or specific degeneration of different types of neurons in central nervous system. Currently, there are no more effective treatments against these diseases. Traditional Chinese medicine (TCM) is focused on, which can provide new strategies for the therapy in neurological disorders. TCM, including Chinese herb medicine, acupuncture, and other nonmedication therapies, has its unique therapies in treating neurological diseases. In order to improve the treatment of these disorders by optimizing strategies using TCM and evaluate the therapeutic effects, we have summarized molecular imaging, a new promising technology, to assess noninvasively disease specific in cellular and molecular levels of living models in vivo, that was applied in TCM therapy for neurological diseases. In this review, we mainly focus on applying diverse molecular imaging methodologies in different TCM therapies and monitoring neurological disease, and unveiling the mysteries of TCM.

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