A Study of Automated Data Mining of Medication Rules in Traditional Chinese Medicine Prescriptions Based on Large Language Model

In recent years, data mining using a variety of mathematical models has become a popular research direction in the professional field of Chinese medicine. Large Language Model (LLM) is a deep learning model trained on hundreds of billions (or more) of text data. It has the ability of Natural Language Understanding (NLU) and Natural Language Processing (NLP), and can provide more intuitive and flexible interactive functions. To address the problems of large amount of data, difficult text processing, and high learning threshold in the field of traditional Chinese medicine, this paper proposes a solution to automate the data mining of traditional Chinese medicine prescriptions by human-computer interaction using the NLU model, taking traditional Chinese medicine as an example for the treatment of osteoporosis, and conducting the frequency statistics, clustering analysis, and apriori association rule analysis of traditional Chinese medicine prescriptions through human-computer interactive questioning. The results show that the results of automated analysis are highly similar to the results of traditional manual statistics using spss. The results show that the automated analysis results of the large language model are highly similar to the traditional manual statistics results using spss. This provides a new research method for data mining in the field of traditional Chinese medicine.