Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

计算机科学 编码器 判决 变压器 自然语言处理 人工智能 卷积神经网络 聊天机器人 F1得分 量子力学 操作系统 物理 电压
作者
Yuanyuan Sun,Dongping Gao,Xifeng Shen,Meiting Li,Jiale Nan,Weining Zhang
出处
期刊:JMIR medical informatics [JMIR Publications]
卷期号:10 (4): e35606-e35606 被引量:5
标识
DOI:10.2196/35606
摘要

With the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies.The purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored.The data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original Bidirectional Encoder Representations from Transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods.We found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 69.2795315, respectively. Our composite abutting joint model (RoBERTa-WWM-ext + CNN) had a macro-F1 value of 70.55936311, showing that our model outperformed the other models in the task.The accuracy of the original model can be greatly improved by giving priority to WWM and replacing the word-based mask with unit to classify and label medical entities. Better results can be obtained by effectively optimizing the downstream tasks of the model and the integration of multiple models later on. The study findings contribute to the translation of online consultation information into machine-readable information.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
不必要再讨论适合与否完成签到,获得积分0
1秒前
无情夏寒完成签到 ,获得积分10
2秒前
慕青应助马士全采纳,获得10
3秒前
xuzj应助科研通管家采纳,获得10
3秒前
Rubby应助科研通管家采纳,获得30
4秒前
SciGPT应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
shiizii应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
ludong_0应助科研通管家采纳,获得10
4秒前
YeeYee发布了新的文献求助10
4秒前
冷酷的松思完成签到,获得积分10
4秒前
zgt01发布了新的文献求助10
5秒前
zhang完成签到,获得积分10
5秒前
江中完成签到 ,获得积分10
7秒前
7秒前
阿玖完成签到 ,获得积分10
8秒前
jiaolulu发布了新的文献求助10
10秒前
踏雪飞鸿完成签到,获得积分10
11秒前
hannah完成签到,获得积分10
11秒前
songvv发布了新的文献求助10
12秒前
一一一应助Bin_Liu采纳,获得10
13秒前
麻果完成签到,获得积分10
15秒前
OER完成签到,获得积分10
15秒前
伦语完成签到,获得积分20
15秒前
中陆完成签到,获得积分10
16秒前
17秒前
莫西莫西完成签到,获得积分10
19秒前
21秒前
量子星尘发布了新的文献求助10
22秒前
xjh完成签到,获得积分10
22秒前
22秒前
lbnzd8g完成签到,获得积分10
24秒前
中海完成签到,获得积分10
24秒前
Ww完成签到,获得积分10
24秒前
伶俐不二完成签到,获得积分10
24秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038201
求助须知:如何正确求助?哪些是违规求助? 3575940
关于积分的说明 11373987
捐赠科研通 3305747
什么是DOI,文献DOI怎么找? 1819274
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022