# PVminerLLM: Extracting Structured Patient Voices from Patient-Generated Text Using Large Language Models

> This article introduces the PVminerLLM framework, an innovative system that uses large language models to automatically extract structured patient voice signals from unstructured patient-generated text, providing a new technical approach for patient feedback analysis in the medical field.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-11T21:38:13.000Z
- 最近活动: 2026-06-11T21:49:55.756Z
- 热度: 141.8
- 关键词: 患者声音, 大语言模型, 医疗NLP, 信息提取, LoRA微调, PEFT, 电子病历, 患者反馈
- 页面链接: https://www.zingnex.cn/en/forum/thread/pvminerllm
- Canonical: https://www.zingnex.cn/forum/thread/pvminerllm
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## PVminerLLM: Guide to Extracting Structured Patient Voices Using Large Language Models

### Core Views
PVminerLLM is an innovative framework that uses large language models to automatically extract structured patient voice signals from unstructured patient-generated text. It addresses the limitations of traditional questionnaires and provides a new path for patient feedback analysis in the medical field. This project is open-source, offering pre-trained models of various scales to support multi-scenario applications.

### Project Basic Information
- Original author/maintainer: SarielMa
- Source platform: GitHub
- Release time: June 11, 2026
- Original link: https://github.com/SarielMa/PVminerLLM

## Research Background and Core Concepts of Patient Voices

## Research Background
Traditional patient feedback relies on structured questionnaires, which struggle to capture real, personalized expressions. The popularity of internet-based healthcare has led to an explosive growth of unstructured text, but extracting structured patient voices remains a challenge in medical NLP. Thus, PVminerLLM was developed.

## Core Dimensions of Patient Voices
1. **Patient Concerns**: Health issues, treatment doubts, prognosis anxiety, etc., to facilitate doctor-patient communication.
2. **Treatment Experience**: Drug side effects, medical process, healthcare provider attitude, etc., to guide service improvement.
3. **Contextual Signals**: Emotional state, health literacy, social support, etc., to help fully understand patient expressions.

## Technical Architecture and Implementation Methods

## Three-Stage Pipeline Architecture
1. **Supervised Fine-Tuning (SFT)**: Using LoRA/QLoRA techniques from the PEFT library, keeping original model parameters unchanged while training low-rank matrices. Advantages: High parameter efficiency, avoids overfitting, easy deployment; supports multi-GPU distributed training.
2. **Model Merging**: Merge the LoRA adapter back into the base model to generate a dedicated extraction model.
3. **FinBen Evaluation Framework**: Precisely measure accuracy, evaluate performance of different signals, and provide fine-grained error analysis.

## Code and Usage
- Training scripts: `sft_peft_ddp.py` (distributed training), `merge_lora.py` (adapter merging), etc.
- Environment setup: `conda env create -f environment.yml` to activate the `finben_vllm3` environment.
- Training command: `torchrun --nproc_per_node=2 sft_peft_ddp.py` (specify model, dataset path, and other parameters).

## Pre-trained Models and Application Scenarios

## Pre-trained Models (Released on Hugging Face)
- `voice_70b_llama3.3_instruct` (high-precision offline tasks)
- `voice_8b_llama3.1_instruct`
- `voice_3b_llama3.2_instruct`
- `voice_qwen2.5_1.5b_instruct` (real-time applications)

## Application Scenarios
1. **Online Patient Community Analysis**: Extract patient concerns and experiences from forums/social media.
2. **Electronic Health Record (EHR) Information Extraction**: Structured processing of chief complaints and medical history in EHRs to support clinical decision-making.
3. **Satisfaction Survey Enhancement**: Analyze open-ended feedback to identify issues not covered by preset options.
4. **Adverse Drug Reaction Monitoring**: Identify spontaneously reported side effects from patients.

## Technical Contributions and Limitations

## Technical Contributions
1. **Domain-specific fine-tuning strategy**: Designed data construction, prompts, and evaluation metrics for patient voice extraction.
2. **Multi-model scale coverage**: 1.5B to 70B parameters, adapting to different computing resource requirements.
3. **Open-source and reproducible**: Complete code and pre-trained models are open-source, supporting follow-up research.

## Limitations
1. **Data privacy**: Strict desensitization and privacy protection measures are required.
2. **Cross-language adaptability**: Currently focused on English; needs adaptation to multi-language and cultural contexts.
3. **Clinical validation**: Extracted information requires verification by clinical experts for accuracy and relevance.

## Conclusion and Future Outlook

### Conclusion
PVminerLLM combines the capabilities of large language models with medical needs, providing a feasible solution for extracting structured information from massive patient texts and promoting the implementation of patient-centered medical concepts.

### Future Directions
1. Strengthen data privacy and ethical protection.
2. Improve cross-language and cross-cultural adaptability.
3. Conduct clinical validation to ensure the clinical value of extracted information.

This open-source project provides new ideas and tools for the digital transformation of healthcare, and will play an important role in improving medical quality and optimizing resource allocation.