Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2721-2726.DOI: 10.11772/j.issn.1001-9081.2022091388
• 2022 10th CCF Conference on Big Data • Previous Articles Next Articles
Xiaomin ZHOU, Fei TENG(), Yi ZHANG
Received:
2022-09-06
Revised:
2022-10-10
Accepted:
2022-10-11
Online:
2022-12-09
Published:
2023-09-10
Contact:
Fei TENG
About author:
ZHOU Xiaomin, born in 1997, M. S. candidate. Her research interests include natural language processing, automatic International Classification of Diseases (ICD) encoding.Supported by:
通讯作者:
滕飞
作者简介:
周晓敏(1997—),女,河北张家口人,硕士研究生,主要研究方向:自然语言处理、国际疾病分类自动编码基金资助:
CLC Number:
Xiaomin ZHOU, Fei TENG, Yi ZHANG. Automatic international classification of diseases coding model based on meta-network[J]. Journal of Computer Applications, 2023, 43(9): 2721-2726.
周晓敏, 滕飞, 张艺. 基于元网络的自动国际疾病分类编码模型[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2721-2726.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022091388
编码种类 | 编码数 | 编码种类 | 编码数 |
---|---|---|---|
少样本编码 | 440 | 所有编码 | 1 533 |
频繁编码 | 1 093 |
Tab. 1 ICD code division result
编码种类 | 编码数 | 编码种类 | 编码数 |
---|---|---|---|
少样本编码 | 440 | 所有编码 | 1 533 |
频繁编码 | 1 093 |
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
CNN | 43.16 | 39.21 | 41.90 | 96.96 | 3.32 | 5.71 | 4.20 | 80.60 |
BiGRU | 62.33 | 31.33 | 41.70 | 97.10 | 3.22 | 4.63 | 3.80 | 82.20 |
ZAGCNN | 56.60 | 46.61 | 51.13 | 96.87 | 25.75 | 22.34 | 23.93 | 93.03 |
AGM-HT | 57.98 | 43.75 | 52.24 | 96.93 | 27.00 | 22.98 | 24.83 | 93.09 |
MNIC | 58.35 | 47.42 | 52.32 | 96.95 | 27.13 | 23.16 | 24.99 | 93.12 |
Tab. 2 Experimental results of each model on all codes
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
CNN | 43.16 | 39.21 | 41.90 | 96.96 | 3.32 | 5.71 | 4.20 | 80.60 |
BiGRU | 62.33 | 31.33 | 41.70 | 97.10 | 3.22 | 4.63 | 3.80 | 82.20 |
ZAGCNN | 56.60 | 46.61 | 51.13 | 96.87 | 25.75 | 22.34 | 23.93 | 93.03 |
AGM-HT | 57.98 | 43.75 | 52.24 | 96.93 | 27.00 | 22.98 | 24.83 | 93.09 |
MNIC | 58.35 | 47.42 | 52.32 | 96.95 | 27.13 | 23.16 | 24.99 | 93.12 |
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
CNN | 20.00 | 0.08 | 0.15 | 87.41 | 0.08 | 0.23 | 0.11 | 87.61 |
BiGRU | 12.00 | 0.23 | 0.44 | 86.65 | 0.51 | 0.14 | 0.22 | 86.78 |
CAML | 21.67 | 0.98 | 1.88 | 85.68 | 0.73 | 0.74 | 0.73 | 86.23 |
ZAGCNN | 27.27 | 0.23 | 0.45 | 87.05 | 0.17 | 0.10 | 0.12 | 87.19 |
AGM-HT | 26.71 | 14.95 | 19.17 | 91.93 | 19.64 | 14.64 | 16.78 | 90.59 |
MNIC | 40.13 | 16.06 | 22.94 | 95.75 | 19.06 | 14.56 | 16.51 | 95.34 |
Tab. 3 Experimental results of each model on few-shot codes
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
CNN | 20.00 | 0.08 | 0.15 | 87.41 | 0.08 | 0.23 | 0.11 | 87.61 |
BiGRU | 12.00 | 0.23 | 0.44 | 86.65 | 0.51 | 0.14 | 0.22 | 86.78 |
CAML | 21.67 | 0.98 | 1.88 | 85.68 | 0.73 | 0.74 | 0.73 | 86.23 |
ZAGCNN | 27.27 | 0.23 | 0.45 | 87.05 | 0.17 | 0.10 | 0.12 | 87.19 |
AGM-HT | 26.71 | 14.95 | 19.17 | 91.93 | 19.64 | 14.64 | 16.78 | 90.59 |
MNIC | 40.13 | 16.06 | 22.94 | 95.75 | 19.06 | 14.56 | 16.51 | 95.34 |
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
MNIC-MN | 18.00 | 2.04 | 3.66 | 87.79 | 1.75 | 1.48 | 1.60 | 87.59 |
MNIC | 40.13 | 16.06 | 22.94 | 95.75 | 19.06 | 14.56 | 16.51 | 95.34 |
Tab. 4 Ablation experimental results
模型 | Micro | Macro | ||||||
---|---|---|---|---|---|---|---|---|
precision | recall | F1 | AUC | precision | recall | F1 | AUC | |
MNIC-MN | 18.00 | 2.04 | 3.66 | 87.79 | 1.75 | 1.48 | 1.60 | 87.59 |
MNIC | 40.13 | 16.06 | 22.94 | 95.75 | 19.06 | 14.56 | 16.51 | 95.34 |
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