Update README with npj Digital Medicine acceptance (DOI: 10.1038/s41746-025-02139-3)

README.md

@@ -12,13 +12,15 @@ tags:
 datasets:
 - perioperative-complications
 pipeline_tag: text-classification
-paper_url: https://doi.org/10.1101/2025.06.11.25329235
+paper_url: https://doi.org/10.1038/s41746-025-02139-3
 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
 
+> **🎉 This work has been accepted by npj Digital Medicine**
+
 ![PeriComp Performance](figure6b.png)
 *Figure: Performance comparison of fine-tuned models across different sizes*
 
@@ -53,7 +55,7 @@ Perioperative complications affect millions of patients globally, with tradition
 - **High variability** in expert performance across institutions
 - **Cognitive load limitations** with complex documentation
 
-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.
+Our research, published in **npj Digital Medicine** (DOI: [10.1038/s41746-025-02139-3](https://doi.org/10.1038/s41746-025-02139-3)), 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)
 *Figure: Strict performance evaluation requiring exact complication type and severity matching*
@@ -219,16 +221,16 @@ If you use PeriComp in your research, please cite:
 @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},
+  journal={npj Digital Medicine},
+  
   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}
+  doi={10.1038/s41746-025-02139-3},
+  url={https://doi.org/10.1038/s41746-025-02139-3},
+  
 }
 ```
 
-**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)
+**Paper**: [Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning](https://doi.org/10.1038/s41746-025-02139-3)
 
 **Code**: [GitHub Repository - gscfwid/PeriComp](https://github.com/gscfwid/PeriComp)