Development of a General Statistical Analytical System Using Nationally Standardized Medical Information.

In Japan, since the Next Generation Medical Infrastructure Act regarding anonymized medical data contributing to R&D came into force in 2018, it is expected to exploit medical data for R&D. The Millennial Medical Record Project has been collected a large amount of standardized medical data of a number of hospitals stored in a database under the act. In order for users to widely exploit the medical data when carrying out trial-and-error, there is a difficulty of data access because of a highly secured management of non-anonymous medical data.

A random forest algorithm-based approach to capture latent decision variables and their cutoff values.

Although reference intervals (RIs) and clinical decision limits (CDLs) are vital laboratory information for supporting the interpretation of numerical clinical pathology results, there is evidence that RIs and CDLs vary in certain contexts as well as other evidence that RIs and CDLs are flawed. We propose a random forest algorithm-based exploration methodology by using phenotype transformation of independent variables in relation to dependent variables to capture latent decision variables and their cutoff values. We denote certain CDLs within the RIs estimated by an indirect method that affect some diagnostics or outcomes in the context of specific patients' conditions as latent CDLs.