Conditional Entropy Based Retrieval Model in Patient-Carer Conversational Cases

Abstract

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.