# MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning

arXiv: https://arxiv.org/abs/2510.21084 · HTML: https://mlsanity.com/paper/2510.21084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 7 of 7)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations](https://mlsanity.com/paper/2105.02711/) (2021)
- [Parameter-Efficient Fine-Tuning for Foundation Models](https://mlsanity.com/paper/2501.13787/) (2025)
- [Change Matters: Medication Change Prediction with Recurrent Residual Networks](https://mlsanity.com/paper/2105.01876/) (2021)
- [Large Language Model Distilling Medication Recommendation Model](https://mlsanity.com/paper/2402.02803/) (2024)

## Built on by (most cited first, 1 of 1)

- [Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records](https://mlsanity.com/paper/2511.06230/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reasoning capabilities. However, their application to Chinese clinical medication recommendation remains largely unexplored. Existing approaches are primarily developed on English electronic health record datasets and focus on coarse-grained medication code prediction, offering limited support for interpretable clinical decision-making. In this work, we propose MediRec, an explainable LLM-based framework for Chinese medication recommendation from electronic health records. MediRec combines clinically grounded reasoning-chain distillation with reinforcement learning to improve both recommendation accuracy and interpretability. Comprehensive experiments on a Chinese medication recommendation benchmark show that MediRec achieves strong performance, with an F1 score of 0.5813 and a Jaccard score of 0.4626. Further analyses indicate that MediRec generates clinically plausible recommendations with transparent reasoning, demonstrating its effectiveness for explainable medication decision support in Chinese healthcare settings.