Large Language Model and Atrial Fibrillation Recurrence

Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and disorganized electrical activity in the atria. It significantly increases the risk of mortality, stroke, and heart failure. Radiofrequency ablation (RFA) is a first-line treatment for AF, yet the recurrence rate remains high.

Traditional clinical risk factors, such as left atrial diameter, AF duration, and AF type, have been proven to be closely associated with AF recurrence. Additionally, derived scoring systems like the CAAP-AF and APPLE scores have demonstrated good predictive value for post-ablation outcomes. However, these assessment methods still fail to comprehensively account for all relevant factors in AF patients.

Large Language Models (LLMs) are deep learning models trained on vast amounts of textual data, capable of generating natural language text and understanding its meaning. By training on massive datasets, these models can provide in-depth knowledge on various topics and exhibit strong language generation capabilities. Prominent LLMs like GPT-4 and LLaMA have achieved remarkable success in natural language processing (NLP) and are gradually being applied in the medical field. Nevertheless, research on LLM-based AF recurrence risk prediction models remains unexplored. Therefore, this study aims to develop an AF recurrence risk prediction model using LLMs, providing further diagnostic and therapeutic insights for both AF ablation patients and clinical electrophysiologists.

Study Design

1. Data Cleaning

   • Remove noisy data from the text, such as garbled characters, duplicates, and irrelevant symbols.

   • Standardize the text, including unifying date formats, units, abbreviations, etc.

   • Handle missing values by reasonably filling or flagging them based on context.

2. Data Annotation

   • Perform structured annotation on text data, including but not limited to:

     • Entity recognition (e.g., disease names, symptoms, test results, treatment plans).

     • Text paragraph classification (e.g., pre-operative echocardiography texts, dynamic ECG texts, surgical records).

   • Combine manual and automated annotation to ensure quality.

3. Data Desensitization

   • Desensitize sensitive information (e.g., patient names) to ensure data privacy and security.

   • Use encrypted storage and transmission to safeguard data during preprocessing.

4. Data Augmentation

   • Expand the dataset by generating additional training samples through synonym replacement, sentence restructuring, etc.

   • Apply sampling techniques to imbalanced data to ensure balanced model training.

5. Data Format Conversion

   • Convert text data into formats required for model training (e.g., JSON, CSV).

   • Segment or chunk data for easier model input.

6. Model Selection and Configuration

   • Choose suitable pre-trained model versions (e.g., Qwen, Deepseek) for medical text processing.

   • Configure hyperparameters (e.g., learning rate, batch size, training epochs).

7. Transfer Learning and Fine-Tuning

   • Perform transfer learning on large language models using medical text data provided by various centers.

   • Design multi-task learning (MTL) strategies for intelligent diagnosis tasks, optimizing evaluations such as ablation prognosis and survival outcomes for AF patients.

8. Model Training

   • Conduct training on hardware environments provided by major centers or high-performance computing platforms like OnekeyAI.

   • Monitor the training process to ensure model convergence and prevent overfitting.

9. Model Optimization

   • Adjust model architecture and hyperparameters based on training results to improve performance.

   • Incorporate reinforcement learning (RL) mechanisms to refine model outputs through interactions with medical experts.

10. Test Set Construction

    • Partition test sets from text data provided by centers, ensuring alignment with training set distributions.

    • Create diverse test scenarios.

11. Model Performance Evaluation

    • Evaluate the model's predictive performance for late-stage AF recurrence using the following metrics:

      • Accuracy: Consistency between model predictions and ground truth.

      • Recall: Proportion of correctly identified cases among all true cases.

      • F1 Score: Harmonic mean of precision and recall.

      • ROC-AUC: Performance assessment for binary classification tasks.

    • Conduct manual evaluation of model outputs, with medical experts reviewing diagnostic results.

12. Error Analysis and Improvement - Analyze erroneous cases in the test set to identify root causes (e.g., insufficient data, labeling errors, model bias).