A Study of Medical Decision Recommendation Generation and Similarity Fusion Based on CDSS and ChatGPT-4

This paper describes the process of embedding ChatGPT-4 (Large Scale Natural Language Modeling) in a CDSS (Clinical Decision Support System) to generate multiple types of decision recommendations. First, given enquiry data, the CDSS generates a specific type of decision recommendations and gives the questions and answers to ChatGPT-4 to generate the related type of decision recommendations. To categorize the same type of decision recommendations in CDSS and ChatGPT-4 together, Word2Vec model was used to learn the semantic relationships of words in medical texts and evaluate the model. Then, the similarity between the decision recommendations generated by CDSS and ChatGPT-4 is determined by calculating the cosine similarity, and a suitable threshold is set to decide whether to fuse these decision recommendations or not. Through this method, the decision recommendations generated by CDSS and ChatGPT-4 can be effectively fused to provide more comprehensive and precise clinical decision support.