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Intelligent Emergency Medical QA System Based on Deep Reinforcement Learning

In: Liss 2021

Author

Listed:
  • Zihao Wang

    (Beijing Jiaotong University)

  • Xuedong Chen

    (Beijing Jiaotong University)

Abstract

This paper mainly focuses on solving the problem of insufficient intelligence of the current emergency medical question answering system, and proposes a solution of deep integration of question answering system and deep reinforcement learning model according to the relevant technology of natural language processing. This paper focuses on the construction and implementation of the interactive environment of deep reinforcement learning, which uses multiple pre trained language models in series, evaluates the environment through the core scoring network of the agent, and decides to return the relevant reply to the user. The structure of several pre training language models is discussed, and the conclusion that dynamic word embedding model with attention mechanism should be used as much as possible, and the complexity of output layer model should be increased.

Suggested Citation

  • Zihao Wang & Xuedong Chen, 2022. "Intelligent Emergency Medical QA System Based on Deep Reinforcement Learning," Lecture Notes in Operations Research, in: Xianliang Shi & Gábor Bohács & Yixuan Ma & Daqing Gong & Xiaopu Shang (ed.), Liss 2021, pages 124-131, Springer.
    • Handle: RePEc:spr:lnopch:978-981-16-8656-6_11
    DOI: 10.1007/978-981-16-8656-6_11
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