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3 October 2022 Automatic summarization of medical conversations based on episodic memory network hierarchical labels
Peng Zheng, Yongbin Liu, Lin Wu
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Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900H (2022) https://doi.org/10.1117/12.2640699
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China
Abstract
Summarizations to the patients’ problems and the doctors’ treatments in the medical conversations can be highly useful, in terms of helping other patients with similar problems have a precise reference for potential medical solutions. The patient's first question in the medical conversation often contains critical information that determines the diagnosis. The previous simple weight division for the role of dialogue determines that doctors and patients' discourses do not affect each other, which makes it incomplete for the model to obtain the important discourses in the dialogue. Therefore, it is proposed to use episodic memory network to enhance the influence of the patient's first sentence on the model's acquisition of important words, and re-divide the sentence role weight according to the first sentence vector. The experiment proves that using episodic memory network can be more sensitive to the information between utterances, acquire important utterances more accurately, and generate high-quality abstracts.
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Peng Zheng, Yongbin Liu, and Lin Wu "Automatic summarization of medical conversations based on episodic memory network hierarchical labels", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900H (3 October 2022); https://doi.org/10.1117/12.2640699
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KEYWORDS
Computer programming
Performance modeling
Data modeling
Neural networks
Systems modeling
Data hiding
Data processing
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