Data Normalization Techniques and Autocoding Algorithms for the Medical Dictionary for Regulatory Activities (MedDRA)

Abstract

Autocoding systems used for Medical Dictionary for Regulatory Activities (MedDRA) coding typically employ verbatim matching methods that attempt to electronically assign a MedDRA dictionary term to a raw adverse event term based upon explicit spellings. Not all raw terms can be directly matched to verbatim terms, thus, generally leaving a sizeable portion of terms to be manually coded. This paper presents data normalizing techniques and autocoding algorithms based upon orthographic properties that are intended to augment the basic linking procedures used by many electronic autocoding systems. Although MedDRA is highlighted in this paper, the normalization techniques and autocoding algorithms presented are suitable for other medical terminology repositories such as Coding Symbols for a Thesaurus of Adverse Reaction Terms (COSTART), World Health Organisation Adverse Reaction Terminology (WHOART), and International Classification of Diseases (ICD-9), as well as in-house dictionaries developed by various pharmaceutical companies. Autocoding systems that employ data normalization and additional matching algorithms may realize a higher electronic match rate, which directly reduces manual review time. Data from multiple clinical studies along with sample code, examples, and results are provided.