A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models: Algorithm Development and Validation Study

Abstract

Background: Several clinical cases and experiments have demonstrated the effectiveness of traditional Chinese medicine (TCM) formulas in treating and preventing diseases. These formulas contain critical information about their ingredients, efficacy, and indications. Classifying TCM formulas based on this information can effectively standardize TCM formulas management, support clinical and research applications, and promote the modernization and scientific use of TCM. To further advance this task, TCM formulas can be classified using various approaches, including manual classification, machine learning, and deep learning. Additionally, large language models (LLMs) are gaining prominence in the biomedical field. Integrating LLMs into TCM research could significantly enhance and accelerate the discovery of TCM knowledge by leveraging their advanced linguistic understanding and contextual reasoning capabilities.

Objective: The objective of this study is to evaluate the performance of different LLMs in the TCM formula classification task. Additionally, by employing ensemble learning with multiple fine-tuned LLMs, this study aims to enhance classification accuracy.

Methods: The data for the TCM formula were manually refined and cleaned. We selected 10 LLMs that support Chinese for fine-tuning. We then employed an ensemble learning approach that combined the predictions of multiple models using both hard and weighted voting, with weights determined by the average accuracy of each model. Finally, we selected the top 5 most effective models from each series of LLMs for weighted voting (top 5) and the top 3 most accurate models of 10 for weighted voting (top 3).

Results: A total of 2441 TCM formulas were curated manually from multiple sources, including the Coding Rules for Chinese Medicinal Formulas and Their Codes, the Chinese National Medical Insurance Catalog for proprietary Chinese medicines, textbooks of TCM formulas, and TCM literature. The dataset was divided into a training set of 1999 TCM formulas and test set of 442 TCM formulas. The testing results showed that Qwen-14B achieved the highest accuracy of 75.32% among the single models. The accuracy rates for hard voting, weighted voting, weighted voting (top 5), and weighted voting (top 3) were 75.79%, 76.47%, 75.57%, and 77.15%, respectively.

Conclusions: This study aims to explore the effectiveness of LLMs in the TCM formula classification task. To this end, we propose an ensemble learning method that integrates multiple fine-tuned LLMs through a voting mechanism. This method not only improves classification accuracy but also enhances the existing classification system for classifying the efficacy of TCM formula.

Keywords: TCM formula classification; algorithm development; ensemble learning; large language models; traditional Chinese medicine.

Figure 1.. Workflow of traditional Chinese medicine formula classification using large language models. BLOOM: BigScience Large Open-Science Open-Access Multilingual Language Model.

Figure 2.. The average accuracy of each large language model in experiment 1. BLOOM: BigScience Large Open-Science Open-Access Multilingual Language Model.

Figure 3.. Accuracy, weighted precision, weighted recall, and weighted F₁-score and their confusion matrices in experiment 2. (A) Accuracy, weighted precision, weighted recall, and weighted F₁-score of different ensemble voting methods. (B) Confusion matrix of the hard voting method. (C) Confusion matrix of the weighted voting method. ADF: antidryness formula; AF: astringent formula; AHF: antihelminthic formula; BRF: blood-regulating formula; DF: desiccating formula; DIF: digestive formula; FP: formula for purgation; FR: formula for resuscitation; FTCU: formula for treating carbuncle and ulcer; FWD: formula for wind disorder; HCF: heat-clearing formula; QRF: Qi regulated formula; RF: reconciling formula; RPRCRWF: resolving phle-m relieving cough and relieving wheezing formula; RTLLF: resolving turbidity and lowering lipids formula; SBF: supplementing and boosting formula; SF: superficies relieving formula; SHEF: summer-heat-expelling formula; SHLDNF: softening hard lumps and dispelling nodes formula; TF: tranquilization formula; WIF: warming interior formula.

Figure 4.. Results analysis of TCM experts. ADF: antidryness formula; LLM: large language model; SBF: supplementing and boosting formula; TCM: traditional Chinese medicine.