Data Analytics in Dentistry Using R Programming Software

2021 ◽  
Author(s):  
Sriram Thirugnanam
Keyword(s):  
Full Potential ◽  
Dental Practice ◽  
Data Set ◽  
R Programming Language ◽  
Clinical Dataset ◽  
Health Authorities ◽  
Dental Practices ◽  
Programming Software ◽  
R Programming ◽  
Descriptive Statistical Analysis

Dental practices collect numerous amounts of clinical and non-clinical data from their patients. Whether that data has been utilized to its full potential is highly questionable. This study used the R programming language on a five-year simulated dental clinical dataset to statistically analyze various possibilities to improve clinical practice and promote awareness among patients. The data set consists of all possible dental treatments which is offered in routine dental practice. The analysis is based on a single dental practice, unlike yearly statistics published by the health authorities over the entire county or country health data, which cannot address unique requirements and challenges associated with every individual practice and community. Descriptive statistical analysis of the dataset is performed through histograms, scattered plots, and test to normality along with correlation analysis with the plot (Pearson/Spearman depend from p.1) and compared variables with multiple regression analysis, forecasting and finally estimated the accuracy using (MAE, MAPE, R_squared ) and k-fold cv.

scholarly journals Using anticlustering to partition data sets into equivalent parts

2019 ◽  
Author(s):  
Martin Papenberg ◽  
Gunnar W. Klau
Keyword(s):  
Cross Validation ◽  
Item Difficulty ◽  
Large Data ◽  
Real Data ◽  
Psychological Research ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
R Programming Language ◽  
R Programming

Numerous applications in psychological research require that a pool of elements is partitioned into multiple parts. While many applications seek groups that are well-separated, i.e., dissimilar from each other, others require the different groups to be as similar as possible. Examples include the assignment of students to parallel courses, assembling stimulus sets in experimental psychology, splitting achievement tests into parts of equal difficulty, and dividing a data set for cross validation. We present anticlust, an easy-to-use and free software package for solving these problems fast and in an automated manner. The package anticlust is an open source extension to the R programming language and implements the methodology of anticlustering. Anticlustering divides elements into similar parts, ensuring similarity between groups by enforcing heterogeneity within groups. Thus, anticlustering is the direct reversal of cluster analysis that aims to maximize homogeneity within groups and dissimilarity between groups. Our package anticlust implements two anticlustering criteria, reversing the clustering methods k-means and cluster editing, respectively. In a simulation study, we show that anticlustering returns excellent results and outperforms alternative approaches like random assignment and matching. In three example applications, we illustrate how to apply anticlust on real data sets. We demonstrate how to assign experimental stimuli to equivalent sets based on norming data, how to divide a large data set for cross validation, and how to split a test into parts of equal item difficulty and discrimination.

scholarly journals Visualizing balances of compositional data: A new alternative to balance dendrograms

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1278 ◽  
Author(s):  
Thomas P. Quinn
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
High Dimensional Data ◽  
Large Data ◽  
High Dimensional ◽  
Compositional Data Analysis ◽  
Data Set ◽  
R Programming Language ◽  
Common Scale ◽  
R Programming

Balances have become a cornerstone of compositional data analysis. However, conceptualizing balances is difficult, especially for high-dimensional data. Most often, investigators visualize balances with the balance dendrogram, but this technique is not necessarily intuitive and does not scale well for large data. This manuscript introduces the 'balance' package for the R programming language. This package visualizes balances of compositional data using an alternative to the balance dendrogram. This alternative contains the same information coded by the balance dendrogram, but projects data on a common scale that facilitates direct comparisons and accommodates high-dimensional data. By stripping the branches from the tree, 'balance' can cleanly visualize any subset of balances without disrupting the interpretation of the remaining balances. As an example, this package is applied to a publicly available meta-genomics data set measuring the relative abundance of 500 microbe taxa.

scholarly journals An open and extensible framework for spatially explicit land use change modelling: the lulcc R package

2015 ◽  
Vol 8 (10) ◽  
pp. 3215-3229 ◽  
Author(s):  
S. Moulds ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Model Development ◽  
R Package ◽  
Spatially Explicit ◽  
Island Ecosystems ◽  
Data Set ◽  
R Programming Language ◽  
R Programming ◽  
Scientific Results

Abstract. We present the lulcc software package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of alternative models; and (3) additional software is required because existing applications frequently perform only the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a data set included with the package. It is envisaged that lulcc will enable future model development and comparison within an open environment.

scholarly journals beachmat: a Bioconductor C++ API for accessing single-cell genomics data from a variety of R matrix types

2017 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Hervé Pagès ◽  
Mike L. Smith
Keyword(s):  
Single Cell ◽  
Data Access ◽  
Cellular Heterogeneity ◽  
Matrix Representations ◽  
Single Experiment ◽  
Data Set ◽  
R Matrix ◽  
R Programming Language ◽  
R Programming ◽  
Cell Data

AbstractRecent advances in single-cell RNA sequencing have dramatically increased the number of cells that can be profiled in a single experiment. This provides unparalleled resolution to study cellular heterogeneity within biological processes such as differentiation. However, the explosion of data that are generated from such experiments poses a challenge to the existing computational infrastructure for statistical data analysis. In particular, large matrices holding expression values for each gene in each cell require sparse or file-backed representations for manipulation with the popular R programming language. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with simple, sparse and HDF5-backed matrices, amongst others. We perform simulations to examine the performance of beachmat on each matrix representation, and we demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large single-cell data set.

Dental Record Keeping by General Dental Practitioners in Lahore

2021 ◽  
Vol 15 (8) ◽  
pp. 2070-2072
Author(s):  
Farhan Riaz ◽  
Saima Sabir ◽  
Umer Abdullah ◽  
Muhammad Shairaz Sadiq ◽  
Ejaz Husain Sahu ◽  
...  
Keyword(s):  
Data Collection ◽  
Study Data ◽  
Patient Records ◽  
Cross Sectional ◽  
Record Keeping ◽  
Group Practices ◽  
Health Authorities ◽  
Dental Practitioners ◽  
Dental Practices ◽  
Dental Photography

Objective: of this study is to analyze the behavior/attitude of general dental practitioners towards record keeping and quality assessment of patient records found in different dental practices of Lahore. Study design: Cross sectional, Descriptive, Questionnaire based study (Copy of questionnaire attached). Place and Duration of Study: Data collection for this study was conducted in different private dental practices of Lahore from Oct-2017 to Dec-2017. Methods; A random sample of 60 dental practices were selected by means of stratified sampling from different towns of Lahore. Dentists were interviewed and patient records were checked for data collection which is analyzed using SPSS version 23. Results: Interview of 43 dentists and analysis of patient records from their practices revealed that 16 (37.2%) practices have no record at all and even none of the remaining 27 (62.8%) practices. Who claim to have patient records, has any properly completed record. Shows that dentists have got very casual behavior towards record keeping as most of them were not having any records and the remaining ones who claimed to have patient records, were maintaining them in a very poor form. Conclusion: Female dentists, postgraduates and dentists working in group practices and affluent areas were found to have relatively more tendency towards record keeping. Recommendation; Dentist training institutes and health implementing authorities are the main areas which need to be stressed upon for improvement of record keeping. Keywords: (MESH) Record keeping, Dental photography, Dentist, Post-graduate, Health authorities, Affluent areas.

scholarly journals Microrisk Lab: an online freeware for predictive microbiology

2020 ◽  
Author(s):  
Yangtai Liu ◽  
Xiang Wang ◽  
Baolin Liu ◽  
Qingli Dong
Keyword(s):  
Parameter Estimation ◽  
Bacterial Growth ◽  
Model Simulation ◽  
Parameter Determination ◽  
Predictive Microbiology ◽  
Isothermal Conditions ◽  
R Programming Language ◽  
Behavior Simulation ◽  
R Programming ◽  
And Behavior

AbstractMicrorisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and ‘Shinyapps.io’ server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

scholarly journals Optimizing Safe Dental Practice During the COVID-19 Pandemic: Recommendations Based on a Guide Developed for Dental Practices in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Li Li ◽  
Mianyan Zeng ◽  
Xiao Chen ◽  
Shuman Cai ◽  
Cuixia Xu ◽  
...  
Keyword(s):  
Relevant Literature ◽  
Cross Infection ◽  
Dental Practice ◽  
Prevention Measures ◽  
Dental Clinics ◽  
Dental Practices ◽  
Risk Areas ◽  
Public Emergency ◽  
Epidemiological Characteristics ◽  
Emergency Measures

The current global coronavirus disease 2019 (COVID-19) outbreak is still exerting severe global implications, and its development in various regions is complex and variable. The high risk of cross-infection poses a great challenge to the dental practice environment; it is therefore urgent to develop a set of pandemic prevention measures to ensure dental practice safety during the COVID-19 outbreak. Therefore, we combined the epidemiological characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), public emergency measures for COVID-19, characteristics of dental practice, and relevant literature reports to develop a set of dynamic practice measures for dental practices in high-, medium-, and low-risk areas affected by COVID-19. This will help dental practices to achieve standard prevention and ensure their safe and smooth operation during the pandemic. It is hoped that these measures will provide a reference basis for dental hospitals and dental clinics in their care and pandemic prevention work.

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