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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (4): 69-81    DOI: 10.11925/infotech.2096-3467.2021.0712
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Identifying Subtypes of Clinical Trial Diseases with BERT-TextCNN
Yang Lin1,Huang Xiaoshuo1,Wang Jiayang1,Ding Lingling2,3,Li Zixiao2,3(),Li Jiao1()
1Institute of Medical Information/Medical Library, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100020, China
2China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
3Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
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Abstract  

[Objective] This study develops a method to identify disease subtypes based on BERT-TextCNN, which could facilitate cohort selection for clinical trials. [Methods] We transformed the disease subtype identification into a single-label classification task based on BERT-TextCNN. Then, we examined our new model with clinical trials data for strokes from ClinicalTrials.gov. [Results] The BERT-TextCNN based on the LP method yielded the best weighted macro-average F1 value of 0.905 3. It identified stroke subtypes for participants of a clinical trial. [Limitations] More research is needed to evaluate our model with other diseases and data sets. [Conclusions] The proposed method could be an effective approach to identify complex disease subtypes.

Key wordsClinical Trial      Text Classification      BERT-TextCNN      Stroke      Disease Subtype     
Received: 16 July 2021      Published: 12 May 2022
ZTFLH:  TP391  
Fund:Natural Science Foundation of Beijing, China(Z200016)
Corresponding Authors: Li Zixiao,ORCID:0000-0002-4713-5418,Li Jiao,ORCID:0000-0001-6391-8343     E-mail: lizixiao2008@hotmail.com;li.jiao@imicams.ac.cn

Cite this article:

Yang Lin, Huang Xiaoshuo, Wang Jiayang, Ding Lingling, Li Zixiao, Li Jiao. Identifying Subtypes of Clinical Trial Diseases with BERT-TextCNN. Data Analysis and Knowledge Discovery, 2022, 6(4): 69-81.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0712     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I4/69

Workflow of Clinical Trial Disease Subtype Identification
Example Annotations of Inclusion Criteria
Problem Transformation Methods for Clinical Trial Stroke Subtype Identification
Clinical Trial Stroke Subtype Identification Based on BERT-TextCNN
模型 训练类别 轮次 批大小 学习率 补齐长度
GloVe-TextCNN_
LabelPowerset
20 24 5e-3 250
GloVe-TextCNN_IS 20 24 5e-3 300
GloVe-TextCNN GloVe-TextCNN_ICH 20 24 5e-3 300
GloVe-TextCNN_SAH 20 16 5e-3 250
GloVe-TextCNN_TIA 20 16 5e-3 250
BERT_LabelPowerset 5 16 1e-5 250
BERT_IS 5 24 9e-6 150
BERT BERT_ICH 5 16 9e-6 150
BERT_SAH 5 24 1e-5 250
BERT_TIA 5 16 1e-5 250
BERT-TextCNN_
LabelPowerset
5 16 1e-5 250
BERT-TextCNN_IS 5 16 1e-5 250
BERT-TextCNN BERT-TextCNN_ICH 5 24 9e-6 250
BERT-TextCNN_SAH 5 32 1e-5 150
BERT-TextCNN_TIA 5 16 1e-5 250
Model Parameters
临床试验 类型 试验数量
招募状态 完成(Completed) 1 208
招募中(Recruiting) 552
未知状态(Unknown Status) 393
未开始招募(Not yet Recruiting) 237
终止(Terminated) 188
正在进行,非招募中(Active, not Recruiting) 70
撤回(Withdrawn) 62
暂停(Suspended) 25
邀请招募(Enrolling by Invitation) 20
干预措施 器械(Device) 790
药物(Drug) 773
其他(Other) 501
行为(Behavioral) 340
手术(Procedure) 213
生物(Biological) 67
诊断测试(Diagnostic Test) 23
饮食补充(Dietary Supplement) 20
复合产品(Combination Product) 13
基因(Genetic) 8
放射(Radiation) 7
纳入标准长度 (0, 50] 2 122
(50, 100] 346
(100, 150] 136
(150, 200] 68
(200, 250] 38
(250, 300] 19
(350, 400] 9
(300, 350] 7
(450, 500] 6
(500, 600] 3
(400, 450] 1
Distribution of Stroke Clinical Trials
序号 可纳入卒中亚型 训练集 测试集 总数量
1 IS 697 310 1 007
2 IS + ICH 405 162 567
3 IS + ICH + SAH 380 166 546
4 IS + TIA 165 66 231
5 SAH 120 53 173
6 ICH 103 53 156
7 IS + ICH + TIA 15 11 26
8 TIA 11 8 19
9 ICH + SAH 12 3 15
10 IS + ICH + SAH + TIA 10 2 12
11 ICH + TIA 1 0 1
12 IS + SAH 1 1 2
总数量 1 920 835 2 755
Distribution of Stroke Subtype Inclusion Criteria
模型 问题转化方法 加权宏平均精确率 加权宏平均召回率 加权宏平均F1值 汉明损失
GloVe-TextCNN Label Powerset 0.822 9 0.841 3 0.830 6 0.059 3
GloVe-TextCNN Binary Relevance 0.802 1 0.789 2 0.781 7 0.065 9
BERT Label Powerset 0.905 1 0.905 0 0.903 7 0.029 9
BERT Binary Relevance 0.889 8 0.879 9 0.883 7 0.035 9
BERT-TextCNN Label Powerset 0.905 7 0.906 2 0.905 3 0.031 1
BERT-TextCNN Binary Relevance 0.897 2 0.889 7 0.891 7 0.031 1
Overall Performance of Clinical Trial Stroke Subtype Identification
序号 可纳入卒中亚型 LP法 二元相关性法
精确率 召回率 F1值 精确率 召回率 F1值
1 IS 0.960 7 0.945 2 0.952 8 0.969 5 0.940 8 0.954 9
2 IS+ICH 0.838 2 0.906 2 0.870 9 0.782 1 0.897 4 0.835 8
3 IS+ICH+SAH 0.896 1 0.831 3 0.862 5 0.899 3 0.827 2 0.861 7
4 IS+TIA 0.910 4 0.924 2 0.917 3 0.857 1 0.843 8 0.850 4
5 SAH 0.928 6 0.981 1 0.954 1 0.961 5 0.943 4 0.952 4
6 ICH 0.909 1 0.961 5 0.934 6 1.000 0 0.942 3 0.970 3
7 IS+ICH+TIA 0.800 0 0.727 3 0.761 9 0.500 0 0.636 4 0.560 0
8 TIA 0.750 0 0.750 0 0.750 0 0.666 7 0.500 0 0.571 4
9 ICH+SAH 0.500 0 0.333 3 0.400 0 0.250 0 0.333 3 0.285 7
10 IS+ICH+SAH+TIA 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
11 ICH+TIA 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
12 IS+SAH 0.000 0 0.000 0 0.000 0 0.250 0 1.000 0 0.400 0
Performance of Clinical Trial Stroke Subtype Identification Based on BERT-TextCNN
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