基于记忆约束剪枝的中医结构化大模型轻量化方法研究

陆家发; 唐凯; 张国明; 俞晓帆; 顾文琦; 李卓

doi:10.11999/JEIT250909

基于记忆约束剪枝的中医结构化大模型轻量化方法研究

doi: 10.11999/JEIT250909 cstr: 32379.14.JEIT250909

1.
江苏省卫生健康信息中心南京 210003
2.
南京邮电大学通信与信息工程学院南京 210003
3.
南京邮电大学物联网学院南京 210023

基金项目: 国家科技重大专项-慢病防治技术推广行动实施项目(SQ2024AAA030211)，

详细信息

作者简介:
陆家发：男，博士研究生，研究方向为医学自然语言处理与轻量化深度学习

通讯作者:
俞晓帆　20230053@njupt.edu.cn

中图分类号: XXXXX
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- 被引次数: 0
出版历程
- 修回日期: 2026-01-22
- 录用日期: 2026-01-22
- 网络出版日期: 2026-02-11

A Study on Lightweight Method of TCM Structured Large Model Based on Memory-Constrained Pruning

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing210003, China
3.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Funds: National Science and Technology Major Project - Implementation Program for the Promotion of Chronic Disease Prevention and Control Technologies

摘要

摘要: 随着人工智能技术的快速发展，大语言模型正在中医医疗领域广泛试点应用。但基层中医医院部署大模型面临GPU资源受限、中医非结构化病历利用率低的双重痛点。为此，本论文提出一种轻量化的中医病历智能结构化模型。所提模型不仅借助知识蒸馏实现文本编码器轻量化，更为重要是在传统文本编码其中引入多模态融合模块，实现轻量化舌诊图像表征。具体而言，提出了一种基于记忆约束的多模态表征轻量化方法。所提方法将长短时记忆网络作为剪枝决策器，分析历史信息中长时依赖关系，以此来学习并量化多模态表征中特征连接的重要性。在此基础上，引入增强学习方法对舌诊特征提取模型参数进行反向更新，进一步提升剪枝决策的准确性。实验采用多中心21家三甲医院的10500份脱敏中医电子病历及舌像图像关联文本进行训练与验证。所提模型F1-score达91.7%，显存占用3.8GB，推理速度22rec/s，较BERT-Large提升27.2%效率且显存降低75%。消融实验表明，动态批次裁剪对显存节省贡献75%(较BERT-Large基准)，在模型自身消融对比中节省62%显存，中医术语增强词表使罕见实体F1提升6.2%。
- 剪枝轻量化 /
- 中医电子病历 /
- 中医实体识别 /
- 多模态融合
Abstract: Objective The structuring of Traditional Chinese Medicine (TCM) electronic medical records (EMRs) is essential for enabling knowledge discovery, clinical decision support, and intelligent diagnosis. However, two significant barriers exist: (1) TCM EMRs are primarily unstructured free text and often paired with tongue images, which complicates automated processing; and (2) grassroots hospitals typically face limited GPU resources, preventing deployment of large-scale pretrained models. This study aims to resolve these challenges by proposing a lightweight multimodal model based on memory-constrained pruning. The approach is designed to retain near–state-of-the-art accuracy while dramatically reducing memory consumption and computation cost, thereby ensuring practical applicability in resource-limited healthcare settings. Methods A three-stage architecture is established, consisting of an encoder, a multimodal fusion module, and a decoder. For textual inputs, a distilled TinyBERT encoder is combined with a BiLSTM-CRF decoder to extract 23 categories of TCM clinical entities, including symptoms, syndromes, prescriptions, and herbs. For visual inputs, a ResNet-50 encoder processes tongue diagnosis images. A novel memory-constrained pruning strategy is introduced: an LSTM decision network observes convolutional feature maps and adaptively prunes redundant channels while retaining crucial diagnostic features. To expand pruning flexibility, gradient re-parameterization and dynamic channel grouping are employed, with stability ensured through a reinforcement-learning controller. In parallel, INT8 mixed-precision quantization, gradient accumulation, and dynamic batch pruning (DBP) are adopted to reduce memory usage. Finally, a TCM terminology–enhanced lexicon is incorporated into the encoder embeddings to address recognition of rare entities. The entire system is trained end-to-end on paired EMR–tongue datasets (Fig. 1), ensuring joint optimization of multimodal information flow. Results and Discussions Experiments are conducted on 10,500 de-identified EMRs paired with tongue images, collected from 21 tertiary hospitals. On an RTX 3060 GPU, the proposed model achieves an F1-score of 91.7%, with peak GPU memory reduced to 3.8 GB and inference speed improved to 22 records per second (Table 1). Compared with BERT-Large, memory consumption decreases by 75% and throughput increases 2.7×, while accuracy remains comparable. Ablation studies confirm the contributions of each module: the adaptive attention gating mechanism raises overall F1 by 2.8% (Table 2); DBP reduces memory usage by 40–62% with minimal accuracy loss and significantly improves performance on EMRs exceeding 5,000 characters (Fig. 2); and the terminology-enhanced lexicon boosts recognition of rare entities such as “blood stasis” by 6.2%. Moreover, structured EMR fields enable association rule mining, where the confidence of syndrome–symptom relationships increases by 18% (Algorithm 1). These findings highlight three main insights: (1) multimodal fusion with lightweight design yields clinical benefits beyond unimodal models; (2) memory-constrained pruning offers stable channel reduction under strict hardware limits, outperforming traditional magnitude-based pruning; and (3) pruning, quantization, and dynamic batching exhibit strong synergy when co-designed, rather than used independently. Collectively, these results demonstrate the feasibility of deploying high-performing TCM EMR structuring systems in real-world environments with limited computational capacity. Conclusions This work proposes and validates a lightweight multimodal framework for structuring TCM EMRs. By introducing memory-constrained pruning combined with quantization and dynamic batch pruning, the method significantly compresses the visual encoder while maintaining fusion accuracy between text and images. The approach delivers near–state-of-the-art performance with drastically reduced hardware requirements, enabling deployment in regional hospitals and clinics. Beyond immediate efficiency gains, the structured multimodal outputs enrich TCM knowledge graphs and improve the reliability of downstream tasks such as syndrome classification and treatment recommendation. The study thus provides both theoretical and practical contributions: it bridges the gap between powerful pretrained models and the limited hardware of grassroots medical institutions, and establishes a scalable paradigm for lightweight multimodal NLP in medical informatics. Future directions include incorporating additional modalities such as pulse-wave signals, extending pruning strategies with graph neural networks, and exploring adaptive cross-modal attention mechanisms to further enhance clinical applicability.
- Pruning Lightweight /
- TCM Electronic Medical Records /
- TCM Entity Recognition /
- Multimodal Fusion

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图 1 模型架构图

下载: 全尺寸图片幻灯片

图 2 基于记忆约束的多模态融合轻量化算法框

下载: 全尺寸图片幻灯片

图 3 可视化热力图对比图(a)-(d)分别表示原始模型热力图、剪枝conv1、剪枝conv3和全部剪枝热力图

下载: 全尺寸图片幻灯片

表 1 不同模型的性能对比

模型	F1-score (%)	显存占用 (GB)	推理速度 (rec/s)
BERT-Large	92.1	15.2	8
本文模型	91.7	3.8	22
CNN-BiLSTM	86.3	2.1	35
本文模型(-AG)	88.9	3.7	23
本文模型(-DBP)	91.5	6.2	20
T + EfficientNet-B0^[25]	89.3	4.1	28
MobileBERT + MobileNet-V2^[26]	88.7	3.5	32
注：-AG表示移除自适应注意力门控，-DBP表示移除动态批次裁剪，动态批次裁剪的显存节省率62%指对比相同模型未启用该策略时的显存(6.2 GB)。

下载: 导出CSV

表 2 按实体类别细分的F1-score(测试集)

实体类别	具体实体名称	F1-score(%)	数据说明
肛肠科	刻下便血颜色	94.2	枚举型实体(淡红/鲜红/暗红)，标签明确
肛肠科	刻下便后肛内肿物是否脱出	93.8	二元枚举(是/否)，关键词规则清晰
骨科	患处VAS评分	95.6	数值型实体(整数)，格式固定
骨科	健侧腕关节周径(cm)	95.1	decimal型数值，单位明确
呼吸内科	痰色	92.5	枚举型(黄/白/绿等)，存在模糊描述(如“黄白相间”)
呼吸内科	有无发热	93.3	二元枚举(有/无)，时间范围明确
消化内科	刻下腹泻频次	91.8	范围枚举(每日<4次等)，需处理模糊表述(如“5～8次”)
消化内科	刻下有无黏液脓血便	92.1	三元枚举(有/无/未提及)，文本表述直接
证候类	舌象(如“舌淡红”)	88.7	描述性文本，存在模糊性(如“淡红偏暗”)
证候类	脉象(如“脉细涩”)	87.5	术语嵌套(脉象+病机)，表述抽象
注：数据来源于2100份测试集(来自总数据集10500份)，各类实体标注参照《中医诊断学术语》

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表 3 各模块对性能的影响

模块	F1-score 变化	显存变化	长文本处理稳定性
自适应注意力门控	+2.8%	±0%	无影响
动态批次裁剪	±0.2%	-62%	提升至99.6%
术语增强词表	+6.2%	±0%	无影响
注：显存节省率计算公式：$ \Delta _{mem}=\frac{{M}_{base}-{M}_{\text{o}pt}}{{M}_{base}}\text{×}100\text{%} $ 其中：$ {M}_{base} $未使用优化策略时的显存(如无DBP时为6.2 GB)-$ {M}_{opt} $使用优化后的显存(3.8 GB)。

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表 4 剪枝策略对比

剪枝策略	F1-score(%)	显存占用(GB)	推理速度(rec/s)
CNN-BiLSTM+无剪枝	91.7	6.2	18
CNN-BiLSTM+L1范数剪枝	91.2	4.8	20
CNN-BiLSTM+记忆约束剪枝	91.5	3.8	22

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表 5 不同剪枝比例下的模型性能

剪枝比例	F1-score(%)	显存占用(GB)	推理速度(rec/s)
30%	91.2	4.5	19
40%	90.9	4.0	21
50%	90.1	3.5	24
60%	89.5	3.1	27
70%	87.1	2.7	30

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表 6 不同LSTM隐藏层大小下的模型性能

LSTM隐藏层大小	F1-score(%)	模型参数量(M)
64	89.2	58.2
128	89.5	59.1
192	89.5	60.3
256	89.4	61.8

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表 7 不同跨模态注意力头数下的模型性能

注意力头数	F1-score(%)	证候类实体F1-score(%)
4	89.1	86.5
8	89.5	87.4
12	89.6	87.5

下载: 导出CSV

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