一种基于深度神经网络的临床记录ICD自动编码方法

doi:10.11959/j.issn.2096-0271.2020040

Abstract

Abstract:

With the increase in the number of the international classification of diseases (ICD) codes,the difficulty and cost of manual coding based on clinical records have greatly increased,and automatic ICD coding technology has attracted widespread attention.A multi-scale residual graph convolution network automatic ICD coding technology was proposed.This technology uses a multi-scale residual network to capture text patterns of different lengths of clinical text and extracts the hierarchical relationship between labels based on the graph convolutional neural network to enhance the ability of automatic coding.The experimental results on the real medical data set MIMIC-III show that the P@k and Micro-F1 of this method are 72.2% and 53.9%,respectively,which significantly improves the prediction performance.

Key words: ICD coding, multi-scale, residual network, graph convolutional network

CLC Number:

TP391

Yichao DU, Tong XU, Jianhui MA, Enhong CHEN, Yi ZHENG, Tongzhu LIU, Guixian TONG. An automatic ICD coding method for clinical records based on deep neural network[J]. Big Data Research, 2020, 6(5): 3-15.

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References 30

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对比项	样本数/条	平均字符数	平均标签个数/个	标签种类/种
训练集	46 157	1 445	15.68	8 579
验证集	3 280	1 742	17.72	4 051
测试集	3 285	1 727	17.31	4 042

模型	P@k		F1		AUC
模型	8	15	Micro	Marco	Micro	Marco
LR	54.5%	41.7%	29.3%	1.2%	92.5%	57.1%
Bi-GRU	63.5%	46.2%	43.3%	4.2%	97.2%	82.4%
Text-CNN	60.1%	45.5%	43.9%	4.5%	96.9%	81.7%
DR-CAML	69.0%	55.3%	52.5%	7.2%	97.9%	87.2%
CAML	71.4%	55.6%	52.8%	7.2%	97.9%	86.4%
MSResGCN	72.2%	56.8%	53.9%	7.6%	98.3%	87.7%

模型	P@k	F1		AUC
模型	8	Micro	Macro	Micro	Macro
w/o-MSRes	71.7%	53.2%	7.5%	98.1%	87.5%
w/o-GCN	72.0%	53.8%	6.9%	98.0%	87.4%
MSResGCN	72.2%	53.9%	7.6%	98.3%	87.7%

An automatic ICD coding method for clinical records based on deep neural network

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