Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.

ABSTRACT:

Background

Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN).

Objective

Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes.

Methods

We used 2 classification methods: (1) extracting from discharge notes some features (terms, n-gram phrases, and SNOMED CT

SUBMITTER: Lin C 

PROVIDER: S-EPMC5696581 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature