Evaluation of an Automated Mapping from ICD-10 to SNOMED CT

The amount of data in the medical field is constantly increasing. But it is not only the sheer amount of information that is important, but also its quality and type of representation. While nomenclatures such as SNOMED CT (Systematized Nomenclature of Medicine and Clinical Term) are suited for finegrained documentation and modern analysis, much information is also bound in classifications. This fact is often historical, as billing systems are typically based on classifications such as ICD-10 and then also had been used for documentation. Leveraging this information automatically is subject of this paper – i.e. enabling an automatic mapping from ICD-10 to SNOMED CT. Because this mapping provides a large set of SNOMED codes for each ICD-10 concept, the approach is non-trivial. In order to pick the best possible code, we propose to take advantage of the hierarchical structure of the SNOMED system to find the concept which lies closer to all candidates in the target system. In other words, our algorithm searches the lowest common ancestor (LCA) of all candidates. For evaluation, we studied 1692 codes from a real-world dataset. The results are promising and show that the proposed approach achieves good results in the majority of cases.