Contributions: (I) Conception and design: D Gao; (II) Administrative support: D Gao, J Zhu; (III) Provision of study materials or patients: J Zhu; (IV)
BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.
Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.
Objectives: To evaluate the synergistic effects of air pollutants and temperature on the mortality risk of respiratory and circulatory diseases in residents. Methods:A generalized additive model based on time series and a bivariate response surface were conducted to assess the association and the potential interaction between air pollutants and temperature with respiratory and circulatory mortality risks using the causes of death and environmental monitoring data from 2014 to 2018 in a certain region of China.Results: Temperature and mortality consistently showed V-shaped associations, and the optimum temperature is 26℃. PM2.5 and PM10 have a lagging effect on respiratory diseases, and the effect was the largest when the lag time was 2 days, for every 10μg/m³ increase of PM2.5 and PM10, the risk of daily respiratory disease death increased by 0.36% and 0.26%, respectively. The influence of SO2 on respiratory diseases and circulatory diseases reached the maximum of 2.38% and 1.43% in 4 days lag, respectively. NO2 and O3 did not affect respiratory diseases, and had the greatest effect on death from circulatory diseases on the day, reaching 1.06% and -0.40%, respectively. CO did not affect death both from respiratory and circulatory diseases. High concentrations of PM2.5, PM10 and O3 together with low temperatures can lead to a significant increase in deaths from respiratory diseases, suggesting that low temperature may enhance the harmful effect of these three pollutants on respiratory diseases. Except for SO2, low temperature can enhance the death effect of air pollutants on circulatory system diseases. High temperature only interacted with PM10 and SO2 on the risk of death from circulatory diseases.Conclusions: The lag effect of air pollutants was different, and the temperature had a synergistic effect with them, suggesting that health benefits would be achieved by paying more attention to the air pollutants for hospitals, governments as well as the public.
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