基于条件熵的医患对话案例检索模型

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI:10.1109/CBMS.2017.145

M. Pavlidou, Antonis Billis, N. D. Hasanagas, C. Bratsas, Ioannis Antoniou, P. Bamidis

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引用次数: 3

摘要

机器人助手是一种高效、低成本的病人护理解决方案。助理机器人的一个重要方面是与患者成功的沟通和社交。提出了一种新的基于条件熵检索的姿态建模方法和基于Popitz幂的姿态建模方法。该算法在患者与机器人助手的对话交互中以较高的成功率成功检索到合适的答案。并结合条件熵模型和Popitz态度模型来识别医患对话互动中的态度变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Conditional Entropy Based Retrieval Model in Patient-Carer Conversational Cases

Bot Assistants can be an efficient and low-cost solution to Patient Care. One important aspect of Assistant Bots is successful Communication and Socialization with the patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The algorithm successfully retrieves the suitable answer with a high success rate in the patient-Bot Assistant dialogue interaction. Moreover, the Conditional Entropy Model and the Popitz Attitude Model are combined in order to identify Attitude Changes in Dialogue Interactions between patients and doctors.

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来源期刊

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

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