14. analysis of the results � I description of data, group comparability and the choice of tests

2015 ◽  
pp. 157-174
Keyword(s):  
Data Group

data group

2000 ◽  
pp. 349-349
Author(s):  
Martin H. Weik
Keyword(s):  
Data Group

Comparison of healthcare utilization and outcomes by gestational diabetes diagnostic criteria

2018 ◽  
Vol 46 (4) ◽  
pp. 401-409 ◽  
Author(s):  
Julianne R. Lauring ◽  
Allen R. Kunselman ◽  
Jaimey M. Pauli ◽  
John T. Repke ◽  
Serdar H. Ural
Keyword(s):  
Gestational Diabetes ◽  
Healthcare Utilization ◽  
Retrospective Cohort ◽  
Neonatal Intensive Care ◽  
Hospital Admissions ◽  
Neonatal Intensive Care Units ◽  
Maternal Healthcare ◽  
Number Of Patients ◽  
Significant Difference ◽  
Data Group

Abstract Objective: To compare healthcare utilization and outcomes using the Carpenter-Coustan (CC) criteria vs. the National Diabetes Data Group (NDDG) criteria for gestational diabetes mellitus (GDM). Methods: This is a retrospective cohort study. Prior to 8/21/2013, patients were classified as “GDM by CC” if they met criteria. After 8/21/2013, patients were classified as “GDM by NDDG” if they met criteria and “Meeting CC non-GDM” if they met CC, but failed to reach NDDG criteria. “Non-GDM” women did not meet any criteria for GDM. Records were reviewed after delivery. Results: There was a 41% reduction in GDM diagnosed using NDDG compared to CC (P=0.01). There was no significant difference in triage visits, ultrasounds for growth or hospital admissions. Women classified as “Meeting CC non-GDM” were more likely to have preeclampsia than “GDM by CC” women [OR 11.11 (2.7, 50.0), P=0.0006]. Newborns of mothers “Meeting CC non-GDM” were more likely to be admitted to neonatal intensive care units than “GDM by CC” [OR 6.25 (1.7, 33.3), P=0.006], “GDM by NDDG” [OR 5.56 (1.3, 33.3), P=0.018] and “Non-GDM” newborns [OR 6.47 (2.6, 14.8), P=0.0003]. Conclusion: Using the NDDG criteria may increase healthcare costs because while it decreases the number of patients being diagnosed with GDM, it may also increase maternal and neonatal complications without changing maternal healthcare utilization.

scholarly journals National Diabetes Data Group vs Carpenter-Coustan criteria to diagnose gestational diabetes

2011 ◽  
Vol 205 (3) ◽  
pp. 253.e1-253.e7 ◽  
Author(s):  
Erica K. Berggren ◽  
Kim A. Boggess ◽  
Alison M. Stuebe ◽  
Michele Jonsson Funk
Keyword(s):  
Gestational Diabetes ◽  
Data Group

scholarly journals Búsqueda de patrones para mejorar productos y servicios en las bibliotecas

2017 ◽  
Vol 31 (72) ◽  
pp. 209
Author(s):  
Esther Marina Ruiz Lobaina ◽  
Pedro Lázaro Romero Suárez
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Data Management ◽  
Graphical Representation ◽  
Bibliographic Database ◽  
Statistical Algorithms ◽  
Hidden Patterns ◽  
Selective Dissemination Of Information ◽  
Data Group ◽  
Bar Graphs

Este trabajo muestra los resultados alcanzados durante la búsqueda de patrones ocultos, aplicando algoritmos estadísticos, a una base de datos bibliográfica. Para esta investigación se seleccionó el software WinIDAMS v.1.3, que utiliza para el manejo de los datos la construcciónde un dataset IDAMS (BUILD) y la agrupación de datos (AGGREG), para el análisis estadístico los algoritmos Análisis de conglomerados (CLUSFIND) y los diagramas de dispersión (SCAT). Para las salidas de los resultados este software ofrece las tablas multidimensionales, capaces de crear por cada grupo de variables seleccionadas una tabla interna con resultados como la frecuencia y la media aritmética, que fueron las seleccionadas para estas pruebas, mientras que para la representación gráfica de los resultados se decidió utilizar los histogramas porque son gráficas de barras que permiten interpretar de forma muy fácil y rápida el comportamiento de las variables seleccionadas para el análisis. Este estudio encontró patrones a través de la clusterización con los cuales fue posible potenciar los servicios de difusión selectiva de la información y proponer nuevos servicios para que formen parte de los productos que brinda la biblioteca. This work shows the results obtained during the search for hidden patterns using statistical algorithms, in a bibliographic database. For this research the WinIDAMS v.1.3 software, used for data management, building an IDAMS dataset (BUILD) and Data Group (AGGREG) for statistical analysis algorithms, Cluster analysis was selected (CLUSFIND) and scatterplots (SCAT). In addition to the outputs of the results this software provides multidimensional tables, able to create for each group of selected variables, an internal table with results such as frequency and average arithmetic were selected for these tests, while for graphical representation of theresults, it was decided to use histograms, because they are bar graphs that allow us to interpret very easily and quickly, the behavior of the variables selected for analysis. This study found patterns through clustering, with which services could enhance Selective Dissemination of Information and propose new services, to become part of the products offered by the library.

scholarly journals Penetapan Instruktur Diklat Menggunakan Metode Clustering K-Means dan Topsis Pada PT PLN (Persero) Udiklat Jakarta

Petir ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 111-121
Author(s):  
Nurul Dyah Budiana ◽  
Riki Ruli A. Siregar ◽  
Meilia Nur Indah Susanti
Keyword(s):  
Study Data ◽  
Topsis Method ◽  
Group Approach ◽  
Application System ◽  
Cluster Data ◽  
Training Materials ◽  
Group Data ◽  
Learning Development ◽  
Work Done ◽  
Data Group

Instructor is the main aspect that exists in the implementation of the training. The increasing number of instructors and the need for training is also increasing every year there is no system that can help the process of determining quickly and precisely. In need of a method that can classify the instructor data in accordance with the title of training materials and can be assigned instructor each of the training materials and do not ignore aspects of assessment of the instructor. In this study data mining techniques are used to help recommend instructors for each subject matter of the training based on the cluster data group approach. So it can be used in determining the instructor's assignment per training materials in the future. K-Means clustering method is used to group data into clusters by looking at the centroid  value that has been determined. And the Topsis method is used to assign one instructor's name through the rankings of preference values. In this research CRISP-DM method is used as software engineering method system work done in sequence or linearly. In the testing process has been generated if the manual data and data processing if the application system is the same. This application to facilitate the Supervisor and Learning Development staff in setting instructors per training materials.

scholarly journals Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat

2021 ◽  
Vol 6 (2) ◽  
pp. 48
Author(s):  
Solmin Paembonan ◽  
Hisma Abduh
Keyword(s):  
Data Set ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Data Group

Dalam penelitian ini menggunakan metode k-means, metode ini dapat digunakan untuk menjadikan beberapa obat yang mirip menjadi suatu kelompok data tertentu. Salah satu cara untuk mengetahui tingkat kemiripan data adalah melalui perhitungan jarak antar data. Semakain kecil jarak antar data semakin tinggi tingkat kemiripan data tersebut dan sebaliknya semakin besar jarak antar data maka semakin rendah tingkat kemiripannya. Tujuan akhir clustering adalah untuk menentukan kelompok dalam sekumpulan data yang tidak berlabel, karena clustering merupakan suatu metode unsupervised dan tidak terdapat suatu kondisi awal untuk sejumlah cluster yang mungkin terbentuk dalam sekumpulan data, maka dibutuhkan suatu evaluasi hasil clustering. Berdasarkan evaluasi yang dilakukan terhadap hasil clustering dengan nilai dari silhouette coeficient = 0,4854. In this study using the k-means method, this method can be used to make several similar drugs into a certain data group. One way to determine the level of similarity of the data is through the calculation of the distance between the data. The smaller the distance between the data, the higher the level of similarity between the data and vice versa, the greater the distance between the data, the lower the similarity level. For a number of clusters that may be formed in a data set, an evaluation of the results of clustering is needed. Based on the evaluation carried out on the results of clustering with the value of the silhouette coefficient = 0.4854.

scholarly journals IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN KELULUSAN MAHASISWA TEPAT WAKTU

2020 ◽  
Vol 2 (1) ◽  
pp. 1-14
Author(s):  
Torkis Nasution
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Academic Quality ◽  
K Value ◽  
Entrance Exam ◽  
Maximum Accuracy ◽  
Data Clusters ◽  
New Students ◽  
Data Group

The selection was an attempt College to get qualified prospective students. Test data for new students able to describe the quality of academic and connect to graduate on time. Recognizing the academic quality of students is required in the implementation of the lecture to obtain optimal results. Real conditions today, timely graduation has not achieved optimally, need to be improved to reach the limits of reasonableness. Data that has no need to do a classification based on academic quality, in order to obtain predictions timely graduation. Therefore, proposed an effort to resolve the problem by applying the K-Nearest Neighbor algorithm to re-clustering the test result data for new students. The procedure is to determine the amount of data clusters, determining the center point of the cluster, calculate the distance of the object with the centroid, classifying objects. If the new data group calculation results together with the results of calculation of new data group then finished its calculations. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results.  

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