arXiv subjects

Ruotai Li

Publications and source records attributed to Ruotai Li.

4 recordsLinked to original sources

arXiv2025

FMASH: Advancing Traditional Chinese Medicine Formula Recommendation with Efficient Fusion of Multiscale Associations of Symptoms and Herbs

Traditional Chinese medicine (TCM) exhibits remarkable therapeutic efficacy in healthcare through patient-specific formulas. However, current AI-based TCM formula recommendation models and methods mainly focus on data-based textual associations between symptoms and herbs, and have not fully utilized their features and relations at different scales, especially at the molecular scale. To address these limitations, we propose the Fusion of Multiscale Associations of Symptoms and Herbs (FMASH), a novel framework that effectively incorporates the properties of herbs on different scales with clinical symptoms and provides refined embeddings of their multiscale associations. The framework integrates molecular-scale features and macroscopic properties of herbs and combines complex local and global relations in the heterogeneous graph of symptoms and herbs. Moreover, it provides effective representation embeddings of the multiscale features and associations of symptoms and herbs in a unified semantic space. Comprehensive experiments have been conducted on FMASH, and the results demonstrate that our FMASH-based model outperforms the state-of-the-art (SOTA) model on both datasets, confirming the effectiveness of FMASH in building the TCM formula recommendation model. In Dataset1, our model has achieved a significant improvement compared to the SOTA model, with increases of 3.38% in Precision@5, 3.89% in Recall@5, and 3.69% in F1-score@5. In Dataset2, Precision@5, Recall@5, and F1-score@5 increase by 2.64%, 1.92%, and 2.23%, respectively. This work facilitates the application of the AI-based TCM formula recommendation and promotes the innovative development of TCM diagnosis and treatment.

cs.LG↗

arXiv2025

Jingfang: An LLM-Based Multi-Agent System for Precise Medical Consultation and Syndrome Differentiation in Traditional Chinese Medicine

The practice of Traditional Chinese Medicine (TCM) requires profound expertise and extensive clinical experience. While Large Language Models (LLMs) offer significant potential in this domain, current TCM-oriented LLMs suffer two critical limitations: (1) a rigid consultation framework that fails to conduct comprehensive and patient-tailored interactions, often resulting in diagnostic inaccuracies; and (2) treatment recommendations generated without rigorous syndrome differentiation, which deviates from the core diagnostic and therapeutic principles of TCM. To address these issues, we develop \textbf{JingFang (JF)}, an advanced LLM-based multi-agent system for TCM that facilitates the implementation of AI-assisted TCM diagnosis and treatment. JF integrates various TCM Specialist Agents in accordance with authentic diagnostic and therapeutic scenarios of TCM, enabling personalized medical consultations, accurate syndrome differentiation and treatment recommendations. A \textbf{Multi-Agent Collaborative Consultation Mechanism (MACCM)} for TCM is constructed, where multiple Agents collaborate to emulate real-world TCM diagnostic workflows, enhancing the diagnostic ability of base LLMs to provide accurate and patient-tailored medical consultation. Moreover, we introduce a dedicated \textbf{Syndrome Differentiation Agent} fine-tuned on a preprocessed dataset, along with a designed \textbf{Dual-Stage Recovery Scheme (DSRS)} within the Treatment Agent, which together substantially improve the model's accuracy of syndrome differentiation and treatment. Comprehensive evaluations and experiments demonstrate JF's superior performance in medical consultation, and also show improvements of at least 124% and 21.1% in the precision of syndrome differentiation compared to existing TCM models and State of the Art (SOTA) LLMs, respectively.

cs.CL↗

arXiv2020

Numerical Discretization of Variational Phase Field Model for Phase Transitions in Ferroelectric Thin Films

Phase field methods have been widely used to study phase transitions and polarization switching in ferroelectric thin films. In this paper, we develop an efficient numerical scheme for the variational phase field model based on variational forms of the electrostatic energy and the relaxation dynamics of the polarization vector. The spatial discretization combines the Fourier spectral method with the finite difference method to handle three-dimensional mixed boundary conditions. It allows for an efficient semi-implicit discretization for the time integration of the relaxation dynamics. This method avoids explicitly solving the electrostatic equilibrium equation (a Poisson equation) and eliminates the use of associated Lagrange multipliers. We present several numerical examples including phase transitions and polarization switching processes to demonstrate the effectiveness of the proposed method.

physics.comp-ph↗

arXiv2019

Variational Phase Field Formulations of Polarization and Phase Transition in Ferroelectric Thin Films

Electric field plays an important role in ferroelectric phase transition. There have been numerous phase field formulations attempting to account for electrostatic interactions subject to different boundary conditions. In this paper, we develop new variational forms of the phase field electrostatic energy and the relaxation dynamics of the polarization vector that involves a hybrid representation in both real and Fourier variables. The new formulations avoid ambiguities appeared in earlier studies and lead to much more effective ways to perform variational studies and numerical simulations. Computations of polarization switching in a single domain by applying the new formulations are provided as illustrative examples.

physics.comp-ph↗