An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes
An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes
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DOI：
10.1016/j.cmpb.2019.05.024
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发表时间：
2019-08-01
影响因子：
6.1
通讯作者：
Sy, Luke Wicent
中科院分区：
文献类型：
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作者：
Huang, Jinmiao;Osorio, Cesar;Sy, Luke Wicent
背景与目的：在现代医院的多个层面，代码分配都至关重要，从确保准确的计费流程到创建有效的患者护理历史记录皆是如此。然而，编码过程繁琐且具有主观性，需要经过大量培训的医学编码员。本研究旨在评估基于深度学习的系统将临床记录自动映射到ICD - 9医学代码的性能。
方法：本研究的评估侧重于无人工定义规则的端到端学习方法。传统的机器学习算法以及最先进的深度学习方法，如循环神经网络和卷积神经网络，被应用于重症监护医学信息库（MIMIC - III）数据集。对所测试算法的不同设置进行了大量实验。
结果：研究结果表明，基于深度学习的方法优于其他传统机器学习方法。根据我们的评估，最佳模型预测前10个ICD - 9代码的F - 1值为0.6957，准确率为0.8967，预测前10个ICD - 9类别时F - 1值为0.7233，准确率为0.8588。我们的实施在某些评估指标上也优于现有研究。
结论：使用了一组标准指标来评估MIMIC - III数据集上ICD - 9代码分配的性能。所有开发的评估工具和资源均可在线获取，可作为进一步研究的基准。（C）2019爱思唯尔有限公司。保留所有权利。
Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes.Methods: The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Information Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm.Results: Findings showed that the deep learning-based methods outperformed other conventional machine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F-1 and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F-1 and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics.Conclusion: A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research. (C) 2019 Elsevier B.V. All rights reserved.