Chronic Disease Related Entity Extraction in Online Chinese Question and Answer Services

Yan, Zhang; Zhang, Yong; Yin, Yanshen; Xu, Jennifer; Xing, Chunxiao; Chen, Hsinchun

doi:10.1007/978-3-319-29175-8_6

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In the Chinese electronic medical record dataset, CCKS, based on the BERT model, published studies demonstrating that the F1 values for the symptom-sign category all exceeded 95% and the F1 values for the treatment entity category all exceeded 82% [ 31 , 32 ]. Additionally, the entity recognition was superior to the entity recognition in online health communities [ 33 , 34 ]. It has been demonstrated that biomedical experts and the general public differently perceive medical entities in diabetes.…”

Section: Discussionmentioning

confidence: 99%

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Zhou

Liao

et al. 2023

Bioengineering

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The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

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