Automatic Assignment of Medical Codes

Abstract

Natural Language Processing (NLP) is one of the most challenging and rapidly growing fields in artificial intelligence. It is all about deciphering human languages and deriving meaning from them. Some of the commonly used test cases include the classification of sentiments and reviews from text data. In this study, we present different language models to assign medical codes to electronic health records. Medical codes (ICD codes) are used to map diseases, injuries, health conditions and surgical procedures to a set of universally recognisable alphanumeric codes. They have become essential for storing patient records to analysing health statistics. It also has enormous financial importance in the form of medical billings and insurance. However, assigning codes to medical records are typically done manually and is error-prone due to its complexity. This work presents a comparative study of machine learning models to assign ICD codes from given medical text with increasing complexity. We believe this research can act as a baseline for further improvements and research.