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Objective:To analyze the application and effects of hierarchical classification method of teaching in the rotation of resident physicians in department of gastroenterology.Methods:From September 2018 to July 2019, there were 84 residents who were selected as research objects including 55 physicians and 29 non-physicians rotating in the Department of Gastroenterology, The Second Affiliated Hospital Zhejiang University School of Medicine. According to seniority, there were 46 low-seniority physicians and 38 high-seniority physicians, respectively. Their performance and satisfaction were evaluated by hierarchical classification method of teaching. SPSS 21.0 was used for t test and chi-square test. Results:There were no significant differences in the score and passing rate of written examination and medical record writing between the physicians and non-physicians ( P >0.05). The skill operation examination scores of physicians were higher than those of non-physicians, and the interview scores and passing rate of physicians were also higher than those of non-physicians, with significant differences ( P<0.05). There were no significant differences between the scores of written examination, interview, medical record writing and skill operation and the passing rate of residents in the low-seniority physicians and high-seniority physicians ( P >0.05). Furthermore, all residents with different seniority and professional background were satisfied with this teaching method. Conclusion:Hierarchical classification method of teaching can effectively improve the teaching quality of residents in department of gastroenterology, and improve the satisfaction of residents with teaching, which is deserved to be generalization and application.
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Esta nota visa apresentar o software IRAMUTEQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires), desenvolvido por Pierre Ratinaud (2009). Trata-se de um programa informático gratuito, que se ancora no software R e permite diferentes formas de análises estatísticas sobre corpus textuais e sobre tabelas de indivíduos por palavras. Desenvolvido inicialmente em língua francesa, este programa começou a ser utilizado no Brasil em 2013. O dicionário experimental em língua portuguesa encontra-se em fase de aprimoramento, embora já seja bastante adequado. O IRAMU-TEQ possibilita os seguintes tipos de análises: estatísticas textuais clássicas; pesquisa de especificidades de grupos; classificação hierárquica descendente; análises de similitude e nuvem de palavras. Pelo seu rigor estatístico, pelas diferentes possibilidades de análise, interface simples e compreensível, e, sobretudo por seu acesso gratuito, o IRAMUTEQ pode trazer muitas contribuições aos estudos em ciências humanas e sociais, que têm o conteúdo simbólico proveniente dos materiais textuais como uma fonte importante de dados de pesquisa.
This note aims to present the software IRAMUTEQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires), developed by Pierre Ratinaud (2009). This is a free program, anchored in R software; and it allows different means of textual statistics analysis in both textual material and tables (individuals by words). Developed originally in French, this software started to be used in Brazil in 2013. An experimental Portuguese dictionary is being improved, nevertheless it allows sufficiently accurate analyzes. The IRAMUTEQ enables different types of analysis: classical textual statistics; specificities of groups; descending hierarchical classification; analyzes of similarity and word cloud. Because of its statistical accuracy, of the distinct possibilities of analysis it allows us to carry out, of its simple and understandable interface, and especially because it is free; IRAMUTEQ can bring many contributions to humanities and social sciences, which are subject areas accustomed with working with symbolic content derived from textual materials as an important kind of research data.
Esta nota presenta el software IRAMUTEQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires), desarrollado por Pierre Ratinaud (2009). Este es un software gratuito que se basa en el software R y permite diferentes formas de análisis estadísticas de corpus textual y de tablas: individuos x palabras. Desarrollado originalmente en francés, este programa comenzó a ser utilizado en Brasil en 2013. El diccionario experimental de la lengua portuguesa se encuentra actualmente en la mejora, aunque es bastante adecuado. El IRAMUTEQ permite los siguientes tipos de análisis: estadísticas textuales clásicas; la investigación grupos específicos; clasificación jerárquica descendiente; análisis de similitud y la nube de palabras. Por su rigor estadístico, las diferentes posibilidades de análisis, su presentación simple y comprensible, y sobre todo por su acceso libre, el IRAMUTEQ puede traer muchas contribuciones a los estudios de humanidades y ciencias sociales, que tienen el contenido simbólico de los materiales textuales de una fuente de datos importantes de investigación.
Subject(s)
Data Interpretation, Statistical , Data Analysis , SoftwareABSTRACT
Objective To investigate the mixed model in bier-archical classification datas and implementing with mixed model in SAS. Methods Hierarchical classification datas exemplify the mixed model u-sing procedure mixed,and compared with traditional general linear model. Results The example shows the same result between the SAS mixed model and the general linear model. Conclusion SAS MIXED can flexi-bly fit and analysis hieraxchical classification datas.
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OBJECTIVE: The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithmhave been designed to detect P, QRS, Twave, first. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. Furthermore the conventionalmulticlass classificationmethodmay have skewed results to themajority class, because of unbalanced data distribution. METHODS: The Hermite model of the higher order statistics is good characterization methods for recognizing morphological QRS complex. We applied three morphological feature extraction methods for detecting QRS complex: higher-order statistics, Hermite basis functions andHermitemodel of the higher order statistics.Hierarchical scheme tackle the unbalanced data distribution problem. We also employed a hierarchical classification method using support vector machines. RESULTS:We compared classification methods with feature extraction methods. As a result, our mean values of sensitivity for hierarchical classification method (75.47%, 76.16% and 81.21%) give better performance than the conventionalmulticlass classificationmethod (46.16%). In addition, theHermitemodel of the higher order statistics gave the best results compared to the higher order statistics and the Hermite basis functions in the hierarchical classification method. CONCLUSION: This research suggests that the Hermite model of the higher order statistics is feasible for heartbeat feature extraction. The hierarchical classification is also feasible for heartbeat classification tasks that have the unbalanced data distribution.