There are various medical vocabularies used worldwide. For example, RxNorm, ATC, Canada DPD, ICD10CM,ICD9CM, SNOMED, MedDRA, and other ontologies, depending on institute location, time period of diagnosis, local standards, etc. So it’s impossible to use same approaches to make studies based on incoherent patient data without preprocessing. That's why it's agreed to used Standard Vocabularies, which contain coherent content across disparate observational databases.
Our task was to make observational studies executable, regardless of the vocabulary which used as a source.
Implementing the Research Environment cloud-based solution in the form of Medical Vocabularies Standardization allowed our clients to dramatically increase their data-processing potential. Any of their researchers can now conveniently use any of the software they need in conjunction with powerful external facilities that allow them to process much larger volumes of data with greater efficiency.
There are successive components of our method:
These steps allow us to create particular concepts mapping or automated scripts to map a great variety of concepts.
Big Data Science
Relational Data Bases
Hi, we are Sciforce - a company where the integration of various branches of science builds up a powerful force to create robust software solutions. Working at the intersection of Computer Science with other technical, natural and humanitarian sciences let us go beyond traditional IT services and become both technical and scientific forces to our customers.