ObjectiveTo analyze the current situation and international research focuses on the study of medical device risk management. MethodsTo retrieve medical device risk management literature information cited from 2002 to 2011 in PubMed such as high-frequency MeSH; analyze current situation and research focuses of medical device risk management by using bibliometrics, bibliographic item co-occurrence matrix builder (BICOMB), and graphical clustering toolkit (gCluto) for quantitative analysis, high-frequency MeSH term papers cluster visualization analysis. ResultsA total of 7 073 published studies were retrieved, basically suggesting a gradually increasing trend of the number of published papers. The top 3 numbers of first authors' papers referred to three countries: the United States, Britain and Germany, while China ranked twelfth. The top 3 numbers of journal articles referred to the United States, Britain and Holland, while China ranked twenty-second. Twenty journals published more than 50 papers, and all these journals were clinical journals. Thirty-three authors published no less than 5 papers, with the maximum of 18 articles. Totally, there were 124 highfrequency MeSHs. The high-frequency MeSHs were classified into 6 categories by using double cluster analysis: kinds 0 to 4 included risk report, risk analysis, risk assessment and methodology of heart valve prosthesis, coronary stents, peripheral vascular stents, implantable defibrillators and other life support device, surgical repair surgical flaps and minimal invasion surgical device such as laparoscopy; kind 5 focused on safety management, risk control, organization and implementation and other related research based on prevention and control of medical device adverse reaction, medical errors, occupation exposure, and equipment failure. ConclusionThe analysis on international literature on medical device risk management basically shows a gradually increasing trend; most studies published in the clinical medicine journals; research focus on risk assessment, safety management and quality improvement in the application such as angioplasty, artificial prosthesis replacement, plastic surgery, minimally invasive surgery and critical care medicine, and radiology diagnosis and treatment; implantable, life-supported invasive and radiological devices as the main research subject; and characteristics include closely combination between medical device risk management and the application of safe and effective, quality improvement systems for clinical diagnosis and treatment.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.
ObjectiveTo learn the distribution pattern and characteristics of autoimmune pancreatitis research literature, and its worldwide research trend. MethodsPublished data between September 22, 2004 and September 21, 2014 were searched by using the keyword autoimmune pancreatitis in the database of Pubmed. Publication year, journals, authors and research topics were bibliometrically analyzed. The analysis software Bibliographic Item Co-occurrence Matrix Builder was used for cluster analysis on high-frequency keywords. ResultsA total of 1 518 articles on autoimmune pancreatitis were acquired. The amount of published literature rose rapidly in the past 10 years, reaching its peak in the year of 2012. Most of the articles were published by several leading authors in the leading journals. There were 26 keywords with a frequency of more than 30 times, and 4 categories were classified through cluster analysis of these keywords. They were pathology and immunology, imaging, diagnosis and treatment. ConclusionsAttention on autoimmune pancreatitis has been increasing in the recent 10 years. Japanese researchers have been taking the lead. Current research focus is the diagnosis of autoimmune pancreatitis.
This article analyzes the supply and demand data of outpatient resources in a large comprehensive tertiary grade A hospital from 2021 to 2023. Cluster analysis is used to classify the offline outpatient volume of each department and identify five different department categories with different outpatient volume characteristics. Based on the differences in outpatient volume and resource utilization between different categories and departments, this paper explores the supply-demand matching relationship of outpatient resources under normal and emergency states from online and offline outpatient. Based on the dimensions of categories and departments, this paper proposes an outpatient resource planning strategy that takes into account both normal and emergency states, providing a basis for improving the quality and efficiency of outpatient services in large comprehensive tertiary grade A hospitals.
Objective To investigate the phenotyping of COPD by cluster analysis and evaluate the value of this method.Methods 168 COPD patients were enrolled from Beijing Tongren Hospital. Demographic and clinical data, such as, sex, age, body mass index ( BMI) , smoking index, course of disease,exacerbation rate, and comorbidities were collected. Pulmonary function test, emphysema scoring by HRCT,dyspnea by MMRC score, COPD assessment test ( CAT) score, six-minute walk test were performed for each patient during the stable stage. Cluster analysis was conducted using SPSS 13. 0. Results According to the GOLD criteria,5, 75, 75, and 13 patients were classified into GOLD stage 1, 2, 3, and 4, respectively. There was no difference among different stages in sex distribution, BMI, smoking index, hypertension, and cerebral infarction incidence( P gt; 0. 05) , but the differences in age, disease course, dyspnea score, six-minute walk distance, BODE score, CAT score, coronary heart disease, exacerbation rate, and HRCT emphysema visual score were significant( P lt;0. 05) . By cluster analysis,168 patients were finally classified into three groups:younger/mild, older/ severe, and older/moderate. The patients with the same GOLD stage appeared indifferent clusters and the patients belonging to different GOLD stages could be in the same cluster. There were significant differences among three groups in age, BMI, exacerbation rate, dyspnea score, CAT score, and comorbidities. The result showed that HRCT emphysema visual score was also an important index todifferentiate clusters, suggesting that emphysema was an important phenotype of COPD. Conclusions Cluster analysis can classify homogeneous subjects into the same cluster, and heterogeneous subjects into different clusters. The results suggest that COPD phenotyping by cluster analysis is clinically useful and significant.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.
ObjectiveTo investigate the gene expression spectrum of retina and optic nerve after partial injury of optic nerve.MethodsSixty SD rats were randomly divided into 4 groups. The optic nerves of the right eyes were clipped for 6 seconds with a pair of crossaction forceps. The retinae and optic nerves in the operation eye and contralateral sham operation eye were removed 3, 7, 14, and 21 days after the injury to detect gene expression patterns with high-density DNA microarrays.ResultsChanges of a mass of gene expressions were found after the optic nerve injury, and the positive rate of gene expression was 2.35%, 6.48%, 3.82% and 4.09% after 3, 7, 14, 21 days, respectively, and the total positive rate was 11.77%. The functions of positive expression of the gene involved cell survival, cytoskeleton, extracellular matrix and cell adhesion, free radicals and oxidative damage, energy and metabolism, inflammation, neurotransmission and ion transport, signal transduction, structural protein, transcription and translation. Up-or down-regulation of repaired genes was the main part of the changes of gene expression, while the alteredexpression destroy genes was the minor part in the whole gene expression spectrum, in which the up- and down-regulation of expression of repaired genes accounted for 13.98% and 24.73% respectively 7 days after the injury, and the downregulation of expression of repaired genes accounted for 17.20% 14 days after the injury.ConclusionsA mass of gene expression changes occurs after the optic nerve injury, and the comprehensive view on the gene expression pattern following the optic nerve injury is crucial to discover the mechanism of post-injury reaction and regeneration.(Chin J Ocul Fundus Dis, 2005,21:163-166)
Objective To analyze and reveal the domestic and abroad studies on polypoidal choroidal vasculopathy (PCV) by bibliometric. Methods Foreign articles were searched from American Institute of Scientific Information online database of web of science (WOS) database, Chinese articles were searched from China National Knowledge Infrastructure (CNKI) database. The age distribution, national and regional, funding agency and citation of the PCV literatures published before December 31 2012 were analyzed by software Bibexcel and SPSS 17.0. Results Totally 696 literatures were analyzed, including 440 foreign literatures and 256 Chinese literatures. The numbers of foreign literatures were gradually increased since 2000, significantly after 2007, reached the maximum at 2011. The literatures were mainly in English, the number of the literatures of China ranked 4th of all countries. The National Natural Science Foundation of China ranked No.5 of funding agency. Citations gradually increased since 2000, increased significantly after 2008; there were 30 highfrequency subjects, and hot topics were clustered into 5 categories: treatment of PCV, genetics studies of PCV, detection, diagnosis and epidemiology of PCV, relationships between PCV and macular degeneration, related cytokine of PCV. Chinese literatures increased since 2000, significantly after 2006, and reached the maximum at 2010; there were 15 literatures supported by National Natural Science Foundation of China; Citation of Chinese literatures gradually increased since 2001, and reached the maximum at 2012. There were 30 highfrequency subjects, and hot topics were clustered into 5 categories: genetics and cytokine studies of PCV, detection and diagnosis of PCV, treatment of PCV, PCV and macular diseases, PCV and vitreous hemorrhage. Conclusions Literatures of PCV were gradually increased since 2000, abroad and domestic literatures of PCV reached maximum at 2011 and 2010 respectively. The abroad research of PCV focuses on 5 categories: treatment of PCV, genetics studies of PCV, detection, diagnosis and epidemiology of PCV, relationships between PCV and macular degeneration, related cytokine of PCV. The domestic research focuses on genetics and cytokine studies of PCV, detection and diagnosis of PCV, treatment of PCV, PCV and macular diseases, PCV and vitreous hemorrhage.
In the realm of data mining based on modern acupuncture clinical research, the impact of literature features such as literature quality, evidence level, sample size, and clinical efficacy on the quality of data mining outcomes remains uncertain. These issues are significant factors restricting the translational application of data mining research results. We suggest employing both entropy weight and linear weighting techniques to assess the specified indicators. This assessment results in a comprehensive weighted score for acupuncture prescriptions, serving as the foundation for our ensuing data mining endeavors. In this study, migraine research serves as an example to contrast the efficacy of weighted algorithms against that of classical algorithms. The findings demonstrate that the algorithm introduced in this research significantly contributes to studies focusing on the dispersed selection of acupuncture points. Its superiority lies in cluster analysis, where it adeptly discerns potential patterns in the amalgamation of acupoints. This algorithm amalgamates evidence-based acupuncture with data mining processes, providing innovative perspectives that augment the caliber of research in acupuncture data mining. Nonetheless, additional research is essential to corroborate these results.