Experience in developing graphical user interface to R programming language for clinical and experimental data analysis

Aim. To develop the software product for of medical data analysis and public health indicators presentation. Methods. R_MED software - an interface for typical experimental, clinical and laboratory, epidemiologic analysis using the R system opportunities - was developed. Results. Functionally, the program consists of the following blocks: «Load Data», «Settings», «Basic calculations», «Data Mining», «Presentation of health indicators». Interface simplifying is also achieved by the inclusion of only those methods that are most often required in medical data analysis. So, the «Basic calculation» unit includes the following statistical calculations: descriptive statistics for quantitative variables, frequency tables, bar charts and box plots, Pearson’s correlation matrix, Pearson’s linear correlation, Spearman’s rank correlation, Spearman’s correlation matrix, 2D scatter plots, defining the difference in two independent sample groups using Student’s test and the Mann-Whitney test, analysis of variance (ANOVA). In the «Settings» unit, a user can choose a set of variables and observations for analysis, to change the set of features for any value, to add, delete, rename the variable, and optionally customize the «Load Data» mode, the basic calculation, and data output. The program provides the ability to visualize data using «Presentation of health indicators» block in the context of territory, year and variant. Territorial cuts can be differentiated into three levels: municipal, regional, district (Federal District) level. To construct the maps, vector data on all 3 spatial levels are stored in the R_MED system, including the Federal Districts of Russian Federation. Conclusion. Originally developed for research problems solving of pathology risk forecasting, the R_MED program, if configured properly, can also be used in other clinical diagnostic and epidemiological studies to monitor problems of socially significant diseases and of health services, as well as in the preparation of annual statistical reports, including the regional level.

healthcare management, data analysis, statistics, R system, graphical user interface, health indicators presentation, Data Mining