Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment

2017/09/27 by Tal Baumel, Baumel, Tal, Jumana Nassour-Kassis +8 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Machine Learning in Healthcare #Topic Modeling #cs.AI #cs.CL

paper · pdf · doi:10.48550/arxiv.1709.09587

arxiv created 2017/11/20 · arxiv updated 2017/11/22

Abstract

In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows easier error analysis, and suggests future directions for improvement.

Cited by

• Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding
• Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
• Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

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