ICD-9 Tagging of Clinical Notes Using Topical Word Embedding

Abstract

Medical records, which contains text, has been dramatically increasing everyday. This means that there is a greater need of analyzing health information in a better way. And this can be done through document classification in natural language applications. In this study, we describe tagging of patient notes with ICD-9 codes through topical word embedding in deep learning called EnHANs. We formulate this paper as a multi-label, multi-class classification problem to categorize the ICD-9 codes of a dataset with 400,000 critical care unit medical records. Knowing accurate diagnosis using ICD-9 codes is a vital information for billing and insurance claims. We demonstrate that through the use of topical word embedding model, we learn to classify patient notes with their corresponding ICD-9 labels moderately well than single-label classification.