CCS Coding of Discharge Diagnoses via Deep Neural Networks

 

Conference: The 7th Conference on Digital Health

Authors: Chadi Helwe, Shady Elbassuoni, Mirabelle Geha, Eveline HiŠtti, Carla Makhlouf Obermeyer

Abstract:

A standard procedure in the medical domain is to code discharge diagnoses into a set of manageable categories known as the CCS codes. ThŒis is typically done by €first manually coding the discharge diagnoses into the standard ICD codes and then using a one-to-one mapping between ICD and CCS codes. In this paper, we study the applicability of deep learning to perform automatic coding of discharge diagnoses into CCS codes. In particular, we build an LSTM network combined with a dense neural network that uses medically-trained word embeddings to code discharge diagnoses into single-level CCS codes. We also investigate the advantage of mapping discharge diagnoses into UMLS concepts before coding is carried out. Experimental results based on a large dataset of manually coded discharge diagnoses show that our deep-learning model outperforms the state-of-the-art automatic coding approaches and that the mapping to UMLS concepts consistently results in signi€cant improvement in the coding accuracy.
 

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