Application of data mining algorithms in research on traditional Chinese medicine formula / 中国药房

In recent years， data mining algorithms have been widely employed in scientific research within the field of traditional Chinese medicine （TCM）. The data mining algorithms are used to effectively handle and analyze the complex data in TCM formulas， providing a rational explanation for the mechanism of action. This method has proven particularly useful in uncovering patterns of compatibility and frequent combinations of herbs in TCM， thereby enhancing the reliability and accuracy of clinical diagnosis， target screening， and the study of new drugs. This paper reviews and analyzes 147 papers on TCM formula research that utilize data mining algorithms. The results indicate that data mining algorithms play a unique advantage in six sub- areas， including the study on the mechanism of action in TCM formula， the dose-efficacy of TCM formulas， the identification of core drugs pairs/groups， mining the relationships among “formulas-drug-symptom”， the discovery of new formulas， and mining the compatibility law. Notably， association rules and clustering algorithms are the most representative.

Análisis de interacciones de los actores de la red de conocimiento en salud ambiental del Observatorio Nacional de Salud de Colombia

Introducción: Las interrelaciones positivas y negativas entre el hombre y el medioambiente impactan en la salud general de la población, por esto, la gestión del conocimiento y la transformación social, orientadas a la prevención de la exposición a factores de riesgo ambiental y a la creación de ambientes propicios, deben realizarse a través de acciones multidisciplinares intersectoriales, como el trabajo colaborativo de redes del conocimiento. Objetivo: Describir las interacciones entre los actores de la Red de Conocimiento de Salud Ambiental del Observatorio Nacional de Salud de Colombia (ONS), con el fin de promover, mejorar y fortalecer la colaboración, intercambio de información y planificación conjunta de acciones. Metodología: Estudio descriptivo transversal de análisis de redes sociales mediante herramientas de minería de texto del lenguaje de programación R. Se analizaron las categorías de agua y saneamiento, clima, calidad del aire, radiaciones electromagnéticas e intoxicaciones químicas de un corpus documental de 99 textos de los actores de la red general de conocimiento en salud pública del ONS. Se calcularon medidas de centralidad y prestigio y se graficaron redes dirigidas multicapa con Power BI. Resultados: Los actores con mayor centralidad en la red fueron: Ministerio de Salud y Protección Social, Superintendencia de Salud, Profamilia, universidades de Antioquia y La Salle, ONS, Observatorio de Salud Ambiental de Bogotá, Organización Panamericana de la Salud y Organización Mundial de la Salud. Las cinco categorías analizadas presentaron bajas centralidades de grado, y las categorías de agua y clima mostraron mayor participación de los actores (más nodos e interacciones). Conclusiones: El análisis de redes sociales permitió identificar temas relevantes de salud ambiental entre los actores de la red del ONS, además de actores clave para desarrollar espacios de interacción y gestión del conocimiento. Acorde con las limitaciones del análisis, se sugiere la inclusión de aproximaciones bibliométricas para la actualización de las interacciones de la red.

Introduction: Positive and negative interactions between the human beings and the environment have an impact on the general health of the population. Therefore, it is necessary to use knowledge management and social transformation, in order to limit exposure to environmental risk factors by creating a favorable environment for healthcare. This should be carried out through multidisciplinary and intersectorial actions, such as the collaborative work of knowledge networks. Objective: To describe the interactions between the actors within the Environmental Health Knowledge Network Colombia's National Observatory of Health (ONS acronym in Spanish), in order to promote, improve and strengthen collaboration, information exchange and planning of collaborative actions. Methodology: Cross-sectional descriptive study to analyze social interactions through text mining tools by R, programmer language. Categories analyzed: Water and sanitation, climate, air quality, electromagnetic radiation and chemical poisoning. Data source: a documentary corpus of 99 texts done by actors of Environmental Health Knowledge Network of Colombia's ONS. We calculated centrality and prestige measures. We used Power BI in order to plot multi-layered directed networks. Results: Actors with greatest centrality in the network: Ministry of Health and Social Protection, Health Superintendency, Profamilia, Antioquia and La Salle universities, National Health Observatory, Bogota's Observatory of Environmental Health, the Pan American Health Organization and the World Health Organization. The five categories analyzed provides a low centrality degree, and water and climate categories presented greater participation by actors (more nodes and links). Conclusions: Social interactions analysis provides the identification of relevant environmental health issues in Colombia and key actors in order to develop interaction spaces for knowledge management. The analysis had limitations that suggest the inclusion of bibliometric approaches for updating the interactions within the network.

Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud / A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care

Introducción: En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad. Objetivo: Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial. Métodos: La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas. Resultados: El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 por ciento un área bajo la curva ROC de 0,987 en la validación cruzada. Conclusiones: Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud(AU)

Introduction: In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality. Objective: To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques. Methods: The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied. Results: The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2percent and an area under the ROC curve of 0.987 in the cross validation. Conclusions: A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care(AU)

Application of data mining to extract knowledge about the occurrence of fistulas after palatoplasty / Aplicação de mineração de dados para extração de conhecimento sobre ocorrência de fístulas após palatoplastia

Introduction: Data mining techniques expand access to important information for the decision-making process during health care. The objective the study proposes using data mining techniques to identify variables (surgical treatment protocols, patient characteristics, post-surgical complications) associated with fistulas after primary palatoplasty in patients with unilateral transforamen incisor cleft (UTIC). Method: A data set of 222 patients with UTIC without syndromes, operated by four surgeons with Furlow's or von Langenbeck's primary palatoplasty techniques, was analyzed for this study. Two models for detecting the outcome of surgery were induced using data mining techniques (Decision Tree and Apriori). Results: Five rules were selected from a decision tree pointing to some variables as predictors of fistulas associated with primary palatoplasty: infection, cough, hypernasality, and surgeon. Analysis of the model indicates that it correctly classifies 95.9% of occurrences between the absence and presence of fistulas. The second model indicates that the absence of post-surgical complications (infection and fever) and normal speech results (absent hypernasality, without suggestive of velopharyngeal dysfunction) are related to the absence of fistulas. Regarding surgical procedures, the Furlow technique and the Vomer flap were more frequent in patients with fistulas. Conclusion: Data mining techniques, as applied in the present study, pointed to infection and cough, hypernasality, and surgeon and surgical techniques as predictors of fistulas related to primary palatoplasty.

Introdução: As técnicas de mineração de dados ampliam o acesso a informações importantes para o processo de tomada de decisão durante os cuidados com a saúde. O objetivo do estudo propõe a utilização de técnicas de mineração de dados para identificar variáveis (protocolos de tratamento cirúrgico, características do paciente, intercorrências pós-cirúrgicas) associadas à ocorrência de fístulas após palatoplastia primária em pacientes com fissura transforame incisivo unilateral (FTIU). Método: Um conjunto de dados de 222 pacientes com FTIU sem síndromes, operados por quatro cirurgiões com as técnicas de palatoplastia primária de Furlow ou von Langenbeck, foi analisado para este estudo. Dois modelos para detecção do resultado da cirurgia foram induzidos usando técnicas de mineração de dados (Árvore de Decisão e Apriori). Resultados: Cinco regras foram selecionadas de uma árvore de decisão apontando para algumas variáveis como preditivas de fístulas associadas à palatoplastia primária: infecção, tosse, hipernasalidade, cirurgião. A análise do modelo indica que ele classifica corretamente 95,9% das ocorrências entre ausência e presença de fístulas. O segundo modelo indica que a ausência de intercorrências pós-cirúrgicas (infecção e febre) e resultado de fala normal (hipernasalidade ausente, sem sugestivo de disfunção velofaríngea) estão relacionados à ausência de fístulas. Em relação aos procedimentos cirúrgicos, o uso da técnica de Furlow e retalho de Vomer foram mais frequentes nos pacientes com fístulas. Conclusão: Técnicas de mineração de dados, conforme aplicadas no presente estudo, apontaram para infecção e tosse, presença de hipernasalidade, cirurgião e técnica cirúrgica como preditores de fístulas relacionadas à palatoplastia primária.

Forecasting The Risk Of Chronic Kidney Disease Using Artificial Intelligence Approaches

In this Paper With the aid of AI techniques, this study aims to predict the early detection of chronic kidney disease, also known as chronic renal disease, in diabetic patients. It then suggests a decision tree to reach specific conclusions with desired accuracy by evaluating its performance in relation to its specification and sensitivity. Methods: The behaviour of learning algorithms based on a set of data mining indicators affects the models that are produced proportionately. Predicting the future is no longer a difficult task thanks to the promises of predictive analytics in big data and the use of machine learning algorithms, especially for the health sector, which has undergone significant evolution as a result of the development of new computer technologies that gave rise to numerous fields of study research. Many initiatives are made to deal with the explosion of medical data on the one hand, and to learn meaningful information from it, forecast diseases, and anticipate treatments on the other. To extract meaningful information and aid in decision-making, researchers used all the technological advancements, including big data analytics, predictive analytics, machine learning, and learning algorithms.

Design of early warning system for mental health problems based on data mining and database / Projeto de sistema de alerta precoce para problemas de saúde mental com base na mineração de dados e banco de dados / Diseño de un sistema de alerta temprana de problemas de salud mental basado en la minería de datos y en la base de datos

ABSTRACT Introduction: Data mining technology is mainly employed in the era of big data to evaluate the acquired information. Subsequently, reasoning about the data inductively is fully automated to discover possible patterns. Objective: Recently, data mining technology in the national mental health database has deepened and can be effectively used to solve various mental health early warning problems. Methods: For example, it can be applied to mine psychological data and extract the most important features and information. Results: This paper presents the design of an early warning system for mental health problems based on data mining techniques to offer some thoughts on early warning of mental health problems, including data preparation, data mining, results in analysis, and decision tree algorithm. Conclusion: The experimental results indicate that the results of the early warning system in this paper can achieve an accuracy rate of more than 96% with a high accuracy rate. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução: A tecnologia de mineração de dados é empregada principalmente na era da big data para avaliar as informações adquiridas. Posteriormente, raciocinar indutivamente sobre os dados de forma totalmente automatizada para descobrir possíveis padrões. Objetivo: Recentemente, a tecnologia de mineração de dados no banco de dados nacional de saúde mental tem se aprofundado e pode ser efetivamente utilizada para resolver vários problemas de alerta precoce da saúde mental. Métodos: Por exemplo, ela pode ser aplicada para a mineração de dados psicológicos e extrair as características e informações mais importantes. Resultados: Este documento apresenta o projeto de um sistema de alerta precoce para problemas de saúde mental baseado em técnicas de mineração de dados, com o objetivo de oferecer algumas reflexões sobre alerta precoce de problemas de saúde mental, incluindo preparação de dados, mineração de dados, análise de resultados e algoritmo de árvore de decisão. Conclusão: Os resultados experimentais indicam que os resultados do sistema de alerta precoce neste trabalho podem alcançar uma taxa de precisão de mais de 96% com uma alta taxa de precisão. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

Resumen Introducción: La tecnología de minería de datos se emplea principalmente en la era de la big data para evaluar la información adquirida. Posteriormente, razonar inductivamente sobre los datos de forma totalmente automatizada para descubrir posibles patrones. Objetivo: Recientemente, la tecnología de minería de datos en la base de datos nacional de salud mental se ha profundizado y puede ser utilizada eficazmente para resolver varios problemas de alerta temprana de salud mental. Métodos: Por ejemplo, puede aplicarse para minar datos psicológicos y extraer las características e información más importantes. Resultados: Este trabajo presenta el diseño de un sistema de alerta temprana de problemas de salud mental basado en técnicas de minería de datos, con el objetivo de ofrecer algunas reflexiones sobre la alerta temprana de problemas de salud mental, incluyendo la preparación de los datos, la minería de datos, el análisis de los resultados y el algoritmo de árbol de decisión. Conclusión: Los resultados experimentales indican que los resultados del sistema de alerta temprana de este documento pueden alcanzar un índice de precisión superior al 96% con un alto índice de precisión. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Application analysis of digital neural network-based data mining method in maximizing the performance of sports training / Análise de aplicação do método de mineração de dados baseado em rede neural digital na maximização do desempenho do treinamento esportivo / Análisis de la aplicación del método de minería de datos basado en redes neuronales digitales para maximizar el rendimiento del entrenamiento deportivo

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Research on the average age of onset of endometrial cancer based on data mining / 中国医师杂志

Objective:To explore the average age at onset of endometrial cancer (EC) and the differences between domestic and international factors.Methods:Pubmed, Wanfang Database, VIP Information Resource System, and China National Knowledge Infrastructure (CNKI) were selected to extract clinical research data related to EC. Through data mining methods such as frequency analysis and cluster analysis, we compared the differences in the average age of onset of EC between domestic and foreign countries.Results:A total of 280 articles that met the inclusion criteria were selected, and frequency analysis found that the average age of onset of EC in the Chinese population was mostly concentrated under 57 years old, while in European and American countries, it was mainly concentrated above 57 years old. Through cluster analysis, it was found that the average age of onset in China was clustered in one category with most Asian countries, while European and American countries and Australian countries were clustered in another category. Through analysis of domestic and foreign articles, it was found that the average age of onset of EC did not show a significant upward or downward trend with years.Conclusions:There are differences in the average onset age of EC among different countries and regions. The onset age of EC in Asian populations is significantly earlier than that in European and American populations. The average onset age of EC in Chinese populations is 54 years old, and there is no trend towards a younger onset of EC.

Analysis on medication rules of Xin'an medicine for treatment of melancholia administered by physicians in the Ming and Qing dynasties based on R language data mining / 中国基层医药

Objective:To investigate the medication rules of Xin'an medicine for the treatment of melancholia and further analyze the medication ideas of Xin'an physicians in the treatment of melancholia.Methods:The documents of Xin'an physicians treating melancholia in the fifth edition of the Chinese Medical Code and the online database of ancient Chinese medicine were retrieved. Excel was used to extract the prescription information to establish the database. R language was used to analyze the data regarding the medication frequency, nature and taste, association rules, and clustering of the traditional Chinese medicine used in the prescription. Results:A total of 127 effective prescriptions were sorted out, and 177 kinds of Chinese medicines were used with a total medication frequency of 1 031 times. The top three Chinese medicines with the highest frequency of use were Poria cocos (57 times), Licorice (46 times), and Paeonia Lactiflora (40 times). The main nature of herbs was plain and warm nature. The warm herbs were the most frequently used (298 times). The first five flavors of the herbs which were the most used were pungent taste (475 times, 28.70%), bitter taste (459 times, 27.73%), and sweet taste (453 times, 27.37%). The commonly used herbs with confidence coefficient > 0.800 were Licorice + Angelica sinensis, Licorice + Angelica sinensis and Paeonia Lactiflora, Licorice + Bupleurum, Licorice + Atractylodes macrocephala, Cyperus root + Ligusticum Chuanxiong, Angelica sinensis + Atractylodes macrocephala and Licorice, Paeonia Lactiflora + Angelica sinensis and Poria cocos, Licorice + Angelica sinensis and Poria cocos, Licorice + Atractylodes macrocephala and Angelica sinensis, Licorice + Bupleurum and Paeonia Lactiflora, Licorice + Atractylodes macrocephala and Ginseng, Licorice + Ginseng and Angelica sinensis, Cyperus root + Medicated leaven, Ginseng + Astragalus mongholicus, Licorice + Astragalus mongholicus.Conclusion:Xin'an medicine for the treatment of melancholia mainly uses pungent, bitter, sweet, and warm herbs. It can adjust the chill and fever, Yin and Yang of the human body, diminishes the urgency, and regulates the flow of Qi.

Study on the regularity of traditional Chinese medicine in treatment of postembolization syndrome after TACE based on data mining / 药学实践杂志

Objective To explore the regularity of traditional Chinese medicine (TCM) in the treatment of postembolization syndrome (PES) after transcatheter arterial chemoembolization (TACE). Methods CNKI, WANFANG and VIP were used as data sources to search the journals and literatures related to TCM treatment from January 2000 to December 2021. Then, relevant TCM formula or Chinese patent medicines preparations were screened out. The Chinese medicinal materials contained were entered into Excel 2019 table database, and the data were analyzed by SPSS Statistics 21.0 and SPSS Modeler 18.0 statistical software. Results 86 qualified prescriptions were included, containing 181 Chinese medicinal materials, with a total frequency of 942 times. Of the 181 Chinese herbs included, there were 28 herbs with frequency ≥10%, with a total frequency of 587. The top 5 Chinese medicinal materials of frequency were licorice, Poria, Atractylodes, Bupleurum and Astragalus. Among the efficacy classifications, tonifying deficiency drugs, heat-clearing drugs and diuretics were most used. In four properties and five tastes, the top three of four properties were warm, flat and cold, and the top three of five tastes were sweet, bitter and pungent. In the classification of meridians, the first three meridians were spleen meridian, lung meridian and liver meridian. 30 association rules were obtained in association rules analysis, 11 common factors were obtained by factor analysis, 6 clustering combinations were obtained by cluster analysis, and 4 commonly used drug combinations were obtained. Conclusion The prescription drugs for the treatment of PES after TACE were mainly tonic drugs, heat-clearing drugs and diuresis and dampness-draining drugs. The treatment methods were mainly invigorating spleen and replenishing qi, clearing heat and dampness and detoxification.

ADE signal mining and analysis of axitinib based on FAERS database / 中国药房

OBJECTIVE To provide references for the clinical safe use of axitinib. METHODS Adverse drug event （ADE） data for axitinib were collected from the US FDA Adverse Event Reporting System （FAERS） database from the first quarter of 2012 to the fourth quarter of 2022. The data were mined and analyzed by utilizing the ratio-of-reporting-ratio （ROR） method and comprehensive standard method of the United Kingdom’s Medicines and Healthcare Products Regulatory Agency （MHRA） of proportional imbalance measurement. RESULTS A total of 13 962 reports of axitinib-related ADEs were obtained， with patients’ age concentrated in 65-85 years （43.25%）， gender predominantly male （65.23%）， country of reporting predominantly US （60.01%）， and serious ADE outcomes mostly hospitalization or prolonged hospitalization （31.51%）. A total of 172 ADE risk signals were detected， involving 18 system and organ classifications （SOC）， mainly systemic diseases and various reactions at the site of administration （3 749 cases， 30.84%） and gastrointestinal system diseases （2 067 cases， 17.00%）. ADE risk signals that occurred more frequently were generally consistent with the drug instruction， such as diarrhea， fatigue， and hypertension； new ADE risk signals requiring clinical attention were death， immune-mediated nephritis， and PT signals contained in the SOC of various benign， malignant， and tumors of undetermined nature （including cysts and polyps）. CONCLUSIONS For ADEs that occur frequently with axitinib and are already contained in the drug instruction （e.g. hypertension， diarrhea）， they should be adequately evaluated before administration， especially for patients with combined use of immune checkpoint inhibitors and patients with underlying hypertension； for ADEs with stronger signals and newer ADEs （e. g. death， disease progression， tumor progression）， the patient’s disease progression should be closely monitored during the treatment period for potentially fatal ADEs； for its rare ADEs （e. g.immune-mediated nephritis， scrotal ulcer， non-infectious encephalitis）， clinical validation should be further strengthened.

Evaluation of Animal Models of Acute Lung Injury Caused by Non-physical Factors Based on Data Mining / 中国实验方剂学杂志

ObjectiveTo study the characteristics of animal models of acute lung injury caused by non-physical factors， so as to provide a reference for the standardization of the preparation of such animal models and lay a foundation for the research on the pathogenesis and the diagnosis and treatment of acute lung injury. MethodThe articles about the animal experiments of acute lung injury published in the last decade were retrieved from China National Knowledge Infrastructure （CNKI）， Wanfang， SinoMed， VIP， and PubMed with the theme terms of "acute lung injury" and "animal model". The animal species， drugs used in modeling， modeling period， methods used in molding， model standards， and model evaluation indicators were summarized， and Excel was used for the frequency analysis. ResultA total of 338 articles were included in this study. The results of the frequency analysis showed that SD rats/C57BL/6 mice were mainly used to establish the animal models of acute lung injury. Male mice were mostly used for modeling， and the commonly used modeling agent was lipopolysaccharides （LPS）. In most cases， the modeling lasted for 6 h after drug administration. Hematoxylin-eosin staining was mainly used for the observation of histological changes in the lungs， which were taken as the criteria for modeling. The established models were mainly evaluated based on lung dry/wet weight ratio， lung index， morphological changes in the lung tissue， myeloperoxidase （MPO）， superoxide dismutase （SOD）， and levels of inflammatory cytokines in the serum and bronchoalveolar lavage fluid （BALF）. ConclusionThe models of acute lung injury were mostly prepared by intraperitoneal injection of LPS （5 mg·kg-1） in SD rats and tracheal instillation of LPS （5 mg·kg-1） in C57BL/6 mice， which were praised for the simple operation， high success rate， and consistent with the pathogenesis of acute lung injury. This study provides a reference for the basic research on acute lung injury by animal experiments.

Research on Syndrome and Treatment Rules of Famous Doctors in Treating Ulcerative Colitis Based on Data Mining / 中医杂志

ObjectiveTo systematically review the clinical experience of four sessions of Masters of Traditional Chinese Medicine and two sessions of National Famous Chinese Medicine Practitioners in treating ulcerative colitis （UC）. Data mining and analysis were conducted to clarify the diagnosis and treatment ideas and characteristics of prescription used by these famous doctors in treating UC. MethodsRelevant literature on the treatment of UC by renowned doctors was retrieved from the establishment of the database until March 31， 2023. The literature was collected from databases such as China National Knowledge Infrastructure （CNKI）， Wanfang Data Knowledge Service Platform， Chinese Science and Technology Periodicals Database， and China Biomedical Literature Database. The data mining techniques including frequency analysis， association rules， and cluster analysis were conducted using the Ancient and Modern Medical Case Cloud Platform V2.3.5. ResultsA total of 157 literatures were included in this study， including 115 clinical case data. The study found that UC can be categorized into 14 types of syndrome patterns for treatment， including large intestine dampness-heat syndrome （75，65.22%）， syndrome of dampness stagnancy due to spleen deficiency （23， 20.00%）， spleen-kidney yang deficiency syndrome （21， 18.26%）. The main affected organs were the spleen （85， 73.91%） and large intestine （75， 65.22%）， and they were closely related to liver （24， 20.87%） and the kidney （21， 18.26%）. The predominant pathogenic factors were dampness （83， 72.17%） ， heat （80， 69.57%） and qi deficiency （65， 56.52%）. The treatment involved 30 kinds of treatment methods， including heat-clearing and dampness-draining method （75， 65.22%）， pleen-tonifying and qi-boosting method （25，21.74%） and spleen-invigorating and dampness-transforming method （23， 20.00%）. The medication involved 187 ingredients， with the most commonly used being heat-clearing herbs （37， 19.79%） and tonifying herbs （27， 14.44%）. The tastes of the herbs were mostly sweet （85， 45.45%） ， bitter （80， 42.78%） ， and pungent （71， 37.97%）. The association rules revealed 16 high-frequency combinations mainly composed of Huanglian （黄连）， Baishao （白芍） and Gancao （甘草） along with Baizhu （白术）， Fuling （茯苓）， Muxiang （木香） and Danggui （当归）. ConclusionFamous doctors are skilled in diagnosing and treating UC based on the differentiation of the zang-fu organs and qi-blood. The key pathological mechanism is “spleen deficiency as the root， and large intestine damp-heat as the manifestation”. The core treatment approach is “heat-clearing， spleen-tonifying， and dampness-draining”， with the inclusion of “regulating qi and blood， and balancing cold and heat”.

Acupoint selection rules of acupuncture and moxibustion for post-stroke epilepsy based on data mining technology / 中国针灸

OBJECTIVE@#To analyze the acupoint selection rules of acupuncture and moxibustion for post-stroke epilepsy by data mining technology.@*METHODS@#The literature regarding acupuncture and moxibustion for post-stroke epilepsy included in CNKI, VIP, Wanfang, SinoMed and PubMed databases from the establishment of the database to August 1st 2022 was retrieved. Microsoft Excel 2019 software was used to establish a database to conduct the descriptive analysis of acupoints; SPSS Modeler 18.0 Apriori algorithm was used to conduct association rule analysis; high-frequency acupoint co-occurrence network diagrams were drawn by Cytoscape3.9.0 software; SPSS Statistics 25.0 software was used to perform hierarchical cluster analysis on high-frequency acupoints and a tree diagram was drawn.@*RESULTS@#Totally 39 articles were included, and 63 prescriptions of acupuncture and moxibustion were extracted, involving 56 acupoints, with a total frequency of 516 times; the top three acupoints with the highest frequency of use were Baihui (GV 20), Fenglong (ST 40) and Neiguan (PC 6); the selected meridians were mainly the governor vessel, the hand and foot yangming meridians; the selection of acupoints were mostly in the head, neck and lower limbs; in terms of acupoint compatibility, Hegu (LI 4)-Shuigou (GV 26) and Neiguan (PC 6) had the highest confidence degree; The top 20 high-frequency acupoints could be divided into 4 effective clusters.@*CONCLUSION@#Modern acupuncture and moxibustion treatment for post-stroke epilepsy attaches great importance to the use of yang meridians and meridians with enrich qi and blood; the core prescription is Shuigou (GV 26)-Neiguan (PC 6)-Hegu (LI 4)-Baihui (GV 20). In addition, the combination of distant and near acupoints is highly valued to improve clinical efficacy.

The rules of acupoint selection of acupuncture and moxibustion for aphasia based on data mining / 中国针灸

OBJECTIVE@#To explore the rules of acupoint selection for aphasia treated with acupuncture and moxibustion using data mining technology.@*METHODS@#From January 1, 2000 to April 1, 2022, the articles for clinical researches of acupuncture and moxibustion for aphasia published in CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase were searched. Using Microsoft Excel 2021, the database was set up to analyze the use frequency of acupoint, meridian tropism, acupoint distribution and the use of specific points. SPSS26.0 was adopted for factor analysis, SPSS Modeler 18.0 was for association rule analysis of prescriptions, and Gephi 0.9.5 was to plot the co-occurrence network diagrams of acupoints and meridians.@*RESULTS@#A total of 140 articles were collated, including 146 acupuncture and moxibustion prescriptions and 189 acupoints. The total use frequency of these acupoints was 1 211. Lianquan (CV 23), Jinjin (EX-HN 12), Yuye (EX-HN 13), Baihui (GV 20) and Yamen (GV 15) were the top 5 acupoints of the high use frequency for aphasia treated with acupuncture and moxibustion. Among 189 acupoints collected, the extra points and empirical points were mostly selected. The top 3 involved meridians were the governor vessel, the gallbladder meridian of foot-shaoyang and the conception vessel. These acupoints were mostly distributed on the head, face and neck region. The use frequency of five-shu points was the highest among the specific points. The acupoint combinations of high frequency referred to Yuye (EX-HN 13)-Jinjin (EX-HN 12), Yuye (EX-HN 13)-Lianquan (CV 23)-Jinjin (EX-HN 12), and Fengchi (GB 20)-Yuye (EX-HN 13)-Jinjin (EX-HN 12). Factor analysis extracted 10 common factors for acupoint compatibility in treatment of aphasia with acupuncture and moxibustion.@*CONCLUSION@#In clinical treatment of aphasia with acupuncture and moxibustion, the local acupoints are preferred. The core acupoints include Lianquan (CV 23), Jinjin (EX-HN 12), Yuye (EX-HN 13), Baihui (GV 20) and Yamen (GV 15). The acupoint prescription is modified flexibly according to syndrome differentiation to enhance the therapeutic effect.

Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining / 中国药房

OBJECTIVE To carry out data mining on the prescriptions of traditional Chinese medicine （TCM） for the treatment of renal anemia， and to explore the medication rules of TCM in the treatment of renal anemia. METHODS The Chinese and English databases including PubMed， the Cochrane Library， Web of Science， CNKI， Wanfang， VIP and CBM were searched by computer to screen the related literature about TCM in the treatment of renal anemia. Excel software was used to extract prescription information， including frequency of use， property and flavor， meridian tropism and efficacy of single TCM. Association rule analysis and hierarchical clustering analysis were performed for each ingredient of TCM with R language. RESULTS A total of 268 literature were included， involving 169 ingredients with a total use frequency of 3 919 times. Among them， Astragalus memeranaceus， Angelica sinensis， Atractylodes macrocephala， Rheum officinale， Poria cocos， Radix Rehmanniae Praeparata， Radix Codonopsis， Salvia miltiorrhiza and Ligusticum chuanxiong were used more than 100 times. The properties and flavors of the drugs were mainly warm and sweet； meridian tropism mainly targeted spleen， liver and kidney； the function was mainly in replenishing qi， nourishing blood， promoting blood circulation and removing blood stasis， and clearing damp and promoting diuresis， purgation. The result of association rule analysis showed that the core ingredients were A. memeranaceus， A. sinensis， A. macrocephala， P. cocos and S. miltiorrhiza. Hierarchical cluster analysis showed that the clustering results were divided into grade 1， 2 and 3， and the cluster classification of A. memeranaceus and A. sinensis was always the same. CONCLUSIONS The core prescription for treating renal anemia includes A. memeranaceus， A. sinensis， A. macrocephala， P. cocos and S. miltiorrhiza； among them， A. memeranaceus and A. sinensis are at the center of the core prescription and indispensable. The medication rule is mainly guided by drugs for invigorating qi， nourishing blood， strengthening spleen， and removing dampness. According to the characteristics of different symptoms of patients， dialectical treatment is made， and other TCM is added or subtracted on the basis of the core prescription.

Medication Rules of Pinelliae Rhizoma Processed Products in Syndrome Differentiation and Treatment of Insomnia Based on Data Mining / 中国实验方剂学杂志

ObjectiveTo analyze and summarize the medication rules of different Pinelliae Rhizoma processed products in the syndrome differentiation and treatment of insomnia using data mining. MethodThe literature on the treatment of insomnia with Pinelliae Rhizoma was retrieved from the China National Knowledge Infrastructure （CNKI）， VIP， and PubMed databases over the past 10 years. An Excel database was constructed to record the prescriptions of different Pinelliae Rhizoma processed products in the treatment of insomnia. SPSS 26.0 software was used for frequency analysis of traditional Chinese medicine （TCM） syndromes related to insomnia， compatibility of drugs， drug effects， and properties. SPSS 26.0 was also used for cluster analysis， factor analysis， and IBM Modeler 18.0 plugin for association rule analysis of the core compatibility of different Pinelliae Rhizoma processed products and combinations. ResultAfter applying inclusion and exclusion criteria， 125 relevant articles were finally included. The commonly used processed products of Pinelliae Rhizoma in the treatment of insomnia were Pinelliae Rhizoma Praeparatum， Pinelliae Rhizoma Praeparatum cum Zingibere et Alumine， and Pinelliae Rhizoma Praeparatum cum Alumine. Among them， Pinelliae Rhizoma Praeparatum was the most frequently used. All three processed products of Pinelliae Rhizoma were often used for insomnia with such TCM syndromes as phlegm-heat disturbing the heart， phlegm-dampness obstructing the interior， and liver Qi stagnation. The compatible drugs were sweet， bitter， and pungent in flavor， cold in nature， and acted on the lung， spleen， heart， and liver meridians， with functions of nourishing deficiency， clearing heat， and calming the mind. The common prescriptions used were Wendantang， Chaihu Longgu Mulitang， Banxia Xiexintang， and Xiaochaihutang， with doses ranging from 6 to 30 g. The core drug combinations were Pinelliae Rhizoma Praeparatum-Poria-Ziziphi Spinosae Semen， Pinelliae Rhizoma Praeparatum Cum Zingibere et Alumine-Jujubae Fructus-Codonopsis Radix， and Pinelliae Rhizoma Praeparatum Cum Alumine-Scutellariae Radix-Bupleuri Radix. ConclusionThis study， for the first time， analyzed and summarized the compatibility and prescription application rules of commonly used processed products of Pinelliae Rhizoma in the treatment of insomnia from the perspective of TCM syndrome differentiation， which provides a theoretical basis for the rational， safe， and effective use of Pinelliae Rhizoma in the treatment of insomnia in TCM.

Signal mining and analysis of adverse drug events of abatacept based on FAERS database / 中国药房

OBJECTIVE To provide a reference for the safe use of abatacept in clinic. METHODS Based on the United States FDA Adverse Event Reporting System （FAERS） database， the generic name of the drug “abatacept” and the trade name “Orencia” were used as the search keywords to retrieve drug adverse event （ADE） signal of abatacept as primary suspected drug. The reported odds ratio method and proportional reporting ratio method in the proportional imbalance method and Excel 2020 software were used to mine and analyze the signals. RESULTS A total of 93 189 abatacept-induced ADE reports were retrieved， mainly female cases （75.98%）， and the age was mainly concentrated in 18-64 years old （35.17%）； main countries reporting data were the United States （47.41%） and Canada （30.59%）， and the number of reports was generally increasing year by year. A total of 3 092 ADE signals were screened， of which the signals associated with the primary disease were similar to those described in the drug instruction of abatacept， such as rheumatoid arthritis， arthralgia， joint swelling， etc.； followed by ADE signals related to infusion reactions， including pain， fatigue， rash， etc. All selected ADE signals involved 27 system organ classes， mainly involved systemic diseases and drug site conditions， musculoskeletal and connective tissue diseases， injury， poisoning and surgical complications， infections and invasive diseases， gastrointestinal diseases， neurological diseases， respiratory， thoracic and mediastinal diseases， heart diseases， benign， malignant and unspecified tumors and reproductive system and breast diseases， etc. A total of 22 ADE signals were not included in the drug instructions of abatacept among the top 50 ADE signals in the number list of reported cases， including fatigue， drug intolerance， abdominal discomfort， swelling， lupus erythematosus， peripheral swelling， cell sores， diarrhea， elevated liver enzymes and lower respiratory tract infection， etc. CONCLUSIONS In the process of clinical use of abatacept， special attention should be paid to infection and its carcinogenicity， while assessing the risk of respiratory and cardiovascular system diseases in patients； when patients suffer from these two underlying diseases， the pros and cons should be weighed carefully before selecting drug； in addition， the drug-induced ADE in the neurological， gastrointestinal and reproductive system cannot be ignored.

Association rules between hair selenium level and residents' diet in Keshan disease area of Zhangjiakou in 2018 -2020 / 公共卫生与预防医学

Objective To explore the related factors of hair selenium level in Keshan disease area of Zhangjiakou City, Hebei Province, and to provide reliable data basis for consolidating the elimination of Keshan disease. Methods The association rule (Apriori) analysis was carried out on the survey data of hair selenium level of people in Keshan disease endemic areas and non-endemic areas of Zhangjiakou City from 2018 to 2020. By setting the minimum support and confidence, and combining with the lift ratio, the association relationship between the hair selenium level of people in Keshan disease area and the staple food and vegetables of the residents was analyzed. Results A total of 5 strong association rules were generated. The maximum values of support, confidence and lift ratio were 12.22, 93.33 and 1.17, respectively, and the minimum values were 10.00, 90.91 and 1.14, respectively. The moderate level of selenium in the hair of residents in the disease area was related to the consumption of food purchased from other places and a large quantity of vegetables. Conclusion Association rules provide a reference research method for improving hair selenium nutrition level of residents in Keshan disease area of Zhangjiakou City and establishing a long-term mechanism for Keshan disease prevention and control.

Mining and analysis of adverse drug reaction signals of melphalan based on FAERS / 中国药房

OBJECTIVE To mine the adverse drug reaction （ADR） signals of melphalan， so as to provide reference for clinically safe drug use. METHODS Using OpenVigil 2.1 data platform， relative ADR reports of melphalan from the first quarter of 2004 to the second quarter of 2022 in FAERS database were collected； data mining was conducted using the reported odds ratio （ROR） method and Medicines and Healthcare Products Regulatory Agency （MHRA） method of disproportional method. ADR reports were described and classified according to the system organ class （SOC） and preferred term （PT） in Medical Dictionary for Regulatory Activities （24.0 edition）. RESULTS A total of 17 046 ADR reports related to the target drug melphalan were retrieved， and the number of ADR reports showed a fluctuating upward trend； the majority of patients were male （43.28%）， and were concentrated between the ages of 50-＜75 （35.09%）， with the main reporting country being the United States （23.97%）； ADR report involved a total of 22 842 severe outcomes， mainly including hospitalization or extended hospitalization （24.45%）. Totally 403 ADR signals were detected， involving 23 SOC， mainly including blood and lymphatic system diseases （801 cases， 13.77%）， followed by eye organ diseases （755 cases， 12.97%） and infectious and invasive diseases （716 cases， 12.30%）. The ADR signals ranked high in the number of reported cases included febrile neutropenia， diarrhea， fever and mucositis and other PT； PT such as pneumonia， sepsis， vitreous hemorrhage， chorioretinal atrophy， myelodysplastic syndrome were not recorded in drug instructions. The ADR signals with high signal strength ranking included choroidal dystrophy， chorioretinal atrophy， eyeball atrophy and other PT， and above three types of PT were not included in the drug instructions. CONCLUSIONS ADRs caused by melphalan mainly include blood and lymphatic system diseases， eye organ diseases， and infectious and invasive diseases； before using melphalan， it is necessary to evaluate the drug use of patients， and pay close attention to the patient’s blood indicators and eye toxicity reaction， so as to guarantee the safety of treatment.