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Applications of Medical Informatics and Data Analysis Methods

The science of statistics contributes to the development and application of tools for the design, analysis, and interpretation of empirical medical studies. The development of new statistical tools for medical applications depends on the innovative use of statistical inference theory, good understanding of clinical and epidemiological research questions, and an understanding of the importance of statistical software. First, statisticians develop a method in response to a need felt in a particular field of the health sciences, after which the new method is disseminated in the form of presentations, reports, and publications. It is also necessary to develop tools for implementing the method: software and manuals. From this point onwards, the extent to which the procedure is adopted will depend on its usefulness. The broader introduction and acceptance of a new analysis method (as useful as the method might be) into medical and health care publications seems to require the method being incorporated into the standard statistical packages generally used by researchers. In addition, if readers do not understand the mathematics or reporting style, or if the conclusions have been drawn on the basis of advanced mathematics or computationally complex procedures not visible in the data (tables or graphs) presented, then clinicians may not be convinced of the results. The lead time from the description of a new technique to its entering into the practice of medical investigators is long [1]. Unsustainable promises and unfulfillable expectations should be avoided in the context of data mining and machine learning [2]. The broader introduction and expansion of a new analysis method to medical publication seems to require that the method helps to solve a data analysis problem, where basic statistical methods have not been useful or applicable. Simpler classical approaches can often provide elegant and sufficient answers to important questions. This Special Issue on Medical Informatics and Data Analysis was an opportunity for the scientific community to present research on the application and complexity of data analytical methods, and to give insight into new challenges in biostatistics, epidemiology health sciences, dentistry, and clinical medicine. The 13 contributed articles belong to four broad groups: (i) basic statistical methods, (ii) data-oriented practical approaches, (iii) complex machine learning and deep learning predictive algorithms, (iv) medical informatics.