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README.md

@@ -12,11 +12,14 @@ tags:
 datasets:
 - perioperative-complications
 pipeline_tag: text-classification
+paper_url: https://doi.org/10.1101/2025.06.11.25329235
+paper_title: "Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning"
+repository: https://github.com/gscfwid/PeriComp
 ---
 
 # PeriComp: Perioperative Complication Detection LoRA Adaptors
 
-![PeriComp Performance](../figure6b.png)
+![PeriComp Performance](figure6b.png)
 *Figure: Performance comparison of fine-tuned models across different sizes*
 
 ## 🩺 Model Overview
@@ -50,9 +53,9 @@ Perioperative complications affect millions of patients globally, with tradition
 - **High variability** in expert performance across institutions
 - **Cognitive load limitations** with complex documentation
 
-Our research demonstrates that targeted task decomposition combined with LoRA fine-tuning enables smaller models to achieve expert-level diagnostic capabilities while maintaining practical deployability.
+Our research, published as a preprint on [medRxiv](https://doi.org/10.1101/2025.06.11.25329235), demonstrates that targeted task decomposition combined with LoRA fine-tuning enables smaller models to achieve expert-level diagnostic capabilities while maintaining practical deployability.
 
-![Strict Performance Evaluation](../figure7.png)
+![Strict Performance Evaluation](figure7.png)
 *Figure: Strict performance evaluation requiring exact complication type and severity matching*
 
 ## 🚀 Quick Start
@@ -75,66 +78,42 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 base_model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Load PeriComp adaptor
-adaptor_name = "your-username/Qwen3-8B-PeriComp"
+adaptor_name = "gscfwid/Qwen3-8B-PeriComp"
 model = PeftModel.from_pretrained(base_model, adaptor_name)
 
 # Prepare clinical input
-clinical_text = '''
-Patient Demographics: 65-year-old male
-Procedure: Laparoscopic cholecystectomy
-Postoperative Course: POD#2 - Patient reports abdominal pain, 
-fever 38.5°C, elevated WBC count 15,000/μL...
-'''
+clinical_text = """
+# Objective
+The objective is to identify postoperative complications from patient data in medical records, mimicking the diagnostic expertise of a senior surgeon.
 
-# Generate complication assessment
-inputs = tokenizer(clinical_text, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=512)
-result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-```
-
-### Targeted Strategy Usage
+# Diagnostic Criteria
+The diagnostic criteria for the 22 postoperative complications are as follows:
 
-For optimal performance with smaller models, use our targeted strategy:
+{the diagnostic criteria for the 22 postoperative complications}
 
-```python
-# Define complications to assess
-complications = [
-    "acute_kidney_injury",
-    "surgical_site_infection", 
-    "paralytic_ileus",
-    # ... other complications
-]
-
-# Assess each complication individually
-results = {}
-for complication in complications:
-    prompt = f"Assess for {complication}: {clinical_text}"
-    # ... inference code
-    results[complication] = assessment
-```
-
-## 📈 Performance Metrics
+# Guidelines of Output structure
 
-### Validation Results (Micro-averaged F1 Scores)
+The output format is specified as:
+{defined the output structure}
 
-- **Center 1 (Primary)**: Complex tertiary care cases
-- **Center 2 (External)**: Community hospital validation
+# Data of medical records
 
-| Model | Center 1 F1 | Center 2 F1 | Human Expert F1 |
-|-------|-------------|-------------|-----------------|
-| PeriComp-4B | 0.55 | 0.52 | 0.526 |
-| PeriComp-8B | 0.61 | 0.58 | 0.526 |
-| PeriComp-14B | 0.65 | 0.62 | 0.526 |
-| PeriComp-32B | 0.68 | 0.65 | 0.526 |
-| PeriComp-QwQ-32B | 0.70 | 0.67 | 0.526 |
+- {General Information (De-identified)}
+- {Postoperative Medical Record}
+- {Abnormal Test Results}
+- {Examination Results}
+"""
 
-### Key Advantages
+# Prompt preparation format details can be found in the example files:
+# - comprehensive_prompt.json for QwQ 32B adapter
+# - targeted_prompts.json for Qwen 3 adapters
+# Note: Models are trained on Chinese clinical texts; performance on other languages is not validated
 
-✅ **Consistent Performance**: No degradation with document complexity  
-✅ **24/7 Availability**: Continuous monitoring capability  
-✅ **Standardized Assessment**: Eliminates inter-observer variability  
-✅ **Comprehensive Detection**: All 22 EPCO-defined complications  
-✅ **Privacy Compliant**: On-premises deployment option  
+# Generate complication assessment
+inputs = tokenizer(clinical_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
 
 ## 🔧 Technical Details
 
@@ -157,9 +136,16 @@ lora_config = {
 }
 ```
 
+### 💻 Code and Data Access
+
+- **GitHub Repository**: [gscfwid/PeriComp](https://github.com/gscfwid/PeriComp)
+- **Complete Implementation**: Training scripts, evaluation code, and data processing pipelines
+- **Prompt Engineering**: Optimized prompt templates for targeted complication detection
+- **Clinical Data**: Available upon reasonable request through institutional collaboration with appropriate ethical approval
+
 ## 📋 Supported Complications
 
-The models detect and grade 22 perioperative complications:
+The models detect and grade 22 perioperative complications based on European Perioperative Clinical Outcome (EPCO) definitions¹:
 
 1. **Cardiovascular**: Myocardial injury, cardiac arrhythmias
 2. **Respiratory**: Pneumonia, respiratory failure  
@@ -175,6 +161,9 @@ Each complication is graded as:
 - **Moderate**: Significant medical management
 - **Severe**: Life-threatening, intensive intervention
 
+---
+¹ Jammer, I. et al. Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: a statement from the ESA-ESICM joint taskforce on perioperative outcome measures. *Eur J Anaesthesiol* 32, 88-105 (2015). DOI: 10.1097/EJA.0000000000000118
+
 ## 🏥 Clinical Applications
 
 ### Primary Use Cases
@@ -209,26 +198,47 @@ Each complication is graded as:
 - Validate outputs against clinical judgment
 - Consider local adaptation for specific institutional practices
 
+### Data Access
+
+⚠️ **Clinical datasets are not publicly available due to patient privacy protection**
+
+**Data Request Process**:
+- Clinical datasets can be requested from corresponding authors for legitimate research purposes
+- Requests must include detailed research protocol and intended use
+- Institutional ethical approval is required before data sharing
+- Data sharing agreements must comply with local privacy regulations
+- Contact: [email protected] for data access inquiries
+
 ## 📚 Citation
 
 If you use PeriComp in your research, please cite:
 
 ```bibtex
-@article{pericomp2025,
-  title={Enhancing Local Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRa Fine-tuning},
-  author={[Authors]},
-  journal={[Journal]},
-  year={2025}
+@article{gao2025pericomp,
+  title={Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning},
+  author={Gao, Shaowei and Zhao, Xu and Chen, Lihui and Yu, Junrong and Tian, Shuning and Zhou, Huaqiang and Chen, Jingru and Long, Sizhe and He, Qiulan and Feng, Xia},
+  journal={medRxiv},
+  pages={2025.06.11.25329235},
+  year={2025},
+  doi={10.1101/2025.06.11.25329235},
+  url={https://doi.org/10.1101/2025.06.11.25329235},
+  publisher={Cold Spring Harbor Laboratory Press}
 }
 ```
 
+**Paper**: [Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning](https://doi.org/10.1101/2025.06.11.25329235)
+
+**Code**: [GitHub Repository - gscfwid/PeriComp](https://github.com/gscfwid/PeriComp)
+
 ## 📧 Contact & Support
 
 For questions, issues, or collaboration opportunities:
 
 - **Research Team**: Department of Anesthesiology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
 - **Technical Issues**: [email protected]
+- **Clinical Data Requests**: [email protected] (requires ethical approval and institutional collaboration)
 - **Clinical Applications**: Perioperative Complications Detection
+- **Code Repository**: [GitHub Issues](https://github.com/gscfwid/PeriComp/issues) for implementation questions
 
 ## 📄 License