Clinical Summarization with ProphetNet

Fine-tuning ProphetNet for improved clinical dialogue summarization

Overview

Developed an advanced clinical dialogue summarization system by fine-tuning ProphetNet on the MeSum dataset. This project focused on improving ROUGE scores and factual accuracy for clinical conversation summarization, with optimized GPU-based training pipelines in Linux clusters.

Research Objectives

  • Improve ROUGE Scores: Enhanced automatic evaluation metrics for summarization quality
  • Factual Accuracy: Maintained medical accuracy in generated summaries
  • Clinical Relevance: Ensured summaries are clinically meaningful and actionable
  • Performance Optimization: Efficient training and inference on distributed systems

Technical Implementation

Model Architecture

  • ProphetNet Base Model: Leveraged pre-trained transformer architecture
  • Clinical Fine-tuning: Specialized training on medical dialogue datasets
  • Multi-task Learning: Combined summarization with clinical entity recognition
  • Attention Mechanisms: Enhanced focus on clinically relevant information

Training Pipeline

  • GPU Optimization: Efficient utilization of Linux cluster resources
  • Distributed Training: Multi-GPU training for faster convergence
  • Data Preprocessing: Specialized tokenization for medical terminology
  • Evaluation Metrics: Clinical-specific assessment criteria

Key Features

Clinical Focus

  • Medical Terminology: Proper handling of clinical vocabulary and abbreviations
  • Context Preservation: Maintains important medical context in summaries
  • Temporal Information: Preserves chronological order of clinical events
  • Entity Recognition: Identifies and preserves key medical entities

Performance Optimizations

  • Batch Processing: Efficient handling of large clinical datasets
  • Memory Management: Optimized for large model training
  • Parallel Processing: Multi-threaded data loading and preprocessing
  • Model Compression: Techniques for deployment efficiency

Technologies & Tools

  • Python: Core implementation and data processing
  • PyTorch: Deep learning framework and model training
  • Transformers: Hugging Face library for ProphetNet implementation
  • NLP Libraries: spaCy, NLTK for text processing
  • Linux Clusters: High-performance computing environment
  • CUDA: GPU acceleration for training and inference

Dataset & Evaluation

MeSum Dataset

  • Clinical Dialogues: Real-world medical conversation data
  • Diverse Scenarios: Various clinical specialties and conditions
  • Expert Annotations: Professionally annotated summaries for training
  • Quality Metrics: Multiple evaluation criteria for clinical relevance

Evaluation Metrics

  • ROUGE Scores: Standard automatic evaluation metrics
  • Clinical Accuracy: Medical expert evaluation of factual correctness
  • Readability: Assessment of summary clarity and coherence
  • Completeness: Coverage of important clinical information

Research Impact

Clinical Applications

  • Medical Documentation: Automated generation of clinical summaries
  • Decision Support: Quick access to patient information for healthcare providers
  • Quality Assurance: Consistent and comprehensive clinical documentation
  • Time Efficiency: Reduced time for manual documentation tasks

Technical Contributions

  • Model Adaptation: Demonstrated effective fine-tuning for clinical domains
  • Evaluation Framework: Established metrics for clinical summarization quality
  • Performance Optimization: Efficient training strategies for large models
  • Reproducibility: Open methodology for clinical NLP research

Future Directions

  • Multi-modal Integration: Incorporation of visual medical data
  • Real-time Processing: Live summarization during clinical consultations
  • Specialty Adaptation: Domain-specific models for different medical specialties
  • Integration: EHR system integration for seamless clinical workflows

Research Environment

Conducted in collaboration with clinical NLP researchers, utilizing state-of-the-art computing infrastructure. The project involved extensive experimentation with different model architectures and training strategies, contributing to the broader field of clinical natural language processing.