Enhancing Retrieval-Augmented Generation for the Medical Domain: A Sectional Summarization Approach with Google TensorFlow

Abstract:

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful tool for various Natural Language Processing (NLP) tasks, including text summarization. However, traditional chunking methods used in RAG systems for the medical domain can be inefficient due to the complex and often interconnected nature of medical information. This paper proposes a novel approach that leverages sectional summarization within the RAG framework. We demonstrate the effectiveness of this approach for medical text summarization using Google TensorFlow.

Introduction:

RAG systems combine retrieval and generation techniques to produce summaries of factual topics. They typically involve retrieving relevant passages from a source document and then feeding these passages to a generation model to create a coherent summary. However, traditional chunking methods, which split documents into fixed-size segments, may not be suitable for the medical domain. Medical documents often contain intricate relationships between sections, and information crucial for understanding might be spread across different chunks.

RAG This image shows a medical document where relevant sections like diagnoses, treatment plans, or anatomical regions are visually identified. This can represent the sectional identification step in the proposed approach Photo credit to Research Gate

Sectional Summarization for RAG:

This work proposes a novel approach that incorporates sectional summarization within the RAG framework for the medical domain. Here’s the breakdown:

1. Sectional Identification: The system first identifies relevant sections within the medical document using domain-specific techniques like named entity recognition (NER) or pre-trained medical NLP models. These sections could be diagnoses, treatment plans, or specific anatomical regions.
2. Sectional Summarization: Each identified section is then summarized using a summarization model. This can be a pre-trained summarization model fine-tuned on medical text data or a domain-specific summarization model trained from scratch.
3. Enhanced Retrieval: The generated summaries from each section are used for retrieval alongside the original document. This allows the RAG system to focus on the most relevant and informative parts of the document based on the identified sections.
4. Improved Generation: The retrieved summaries and the original document are then fed into the generation model, enabling it to create a more comprehensive and informative summary that leverages the sectional structure of the medical document.

TensorFlow Implementation:

Here’s a glimpse into how this approach can be implemented using TensorFlow:

Python

import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense

def identify_sections(document):
  """
  This function identifies relevant sections in a medical document using named entity recognition (NER) or pre-trained medical NLP models.

  Args:
      document: A string containing the medical document text.

  Returns:
      A list of strings, where each element represents a section identified within the document.
  """
  # Replace with your implementation using NER or pre-trained medical NLP models
  # This example showcases identifying sections based on keywords
  sections = []
  if "diagnosis" in document.lower():
    sections.append("diagnosis")
  if "treatment plan" in document.lower():
    sections.append("treatment plan")
  # Add logic to identify other relevant sections based on your domain knowledge
  return sections

def summarize_section(section):
  """
  This function summarizes a given section of the medical document using a pre-trained or domain-specific summarization model.

  Args:
      section: A string containing the text of a specific section from the document.

  Returns:
      A string containing the summarized text of the section.
  """
  # Replace with your implementation using a pre-trained summarization model or a domain-specific model trained on medical text data
  # This example showcases a basic summarization using string truncation (replace with a proper summarization model)
  return section[:100]  # Truncate the section to first 100 characters (adjust truncation length as needed)

def load_pre_trained_models():
  """
  This function loads pre-trained models for the retriever and generator components of the RAG system.

  Returns:
      A tuple containing two elements:
          - retriever: The pre-trained passage retrieval model.
          - generator: The pre-trained text generation model.
  """
  # Replace with your pre-trained model loading logic (e.g., TensorFlow Hub or custom model loading)
  retriever = tf.keras.Model(...)  # Placeholder for passage retrieval model
  generator = tf.keras.Sequential([
      LSTM(128),  # Placeholder LSTM layer for processing retrieved information
      Dense(..., activation='softmax')  # Placeholder output layer for generating summary
  ])
  return retriever, generator

# Load document and perform sectional processing
document = "Load your medical document here"
sections = identify_sections(document)
section_summaries = [summarize_section(section) for section in sections]

# Load pre-trained RAG models (retriever and generator)
retriever, generator = load_pre_trained_models()

# Combine retrieved summaries and document for generation
retrieved_data = tf.concat([document, *section_summaries], axis=0)
generated_summary = generator(retriever(retrieved_data))

# Print the generated summary
print(generated_summary.numpy())

Program Explanation

• Docstring Addition: Docstrings are added to the identify_sections and summarize_section functions to improve code readability and understanding.
• Placeholder Implementation: While the original code had placeholder comments, this version clarifies the expectation of replacing these sections with actual implementations using NER or pre-trained medical NLP models for section identification and a proper summarization model for section summarization.
• Sample Implementation: The summarize_section function now includes a basic example using string truncation (for illustration purposes). This should be replaced with a proper summarization model trained on medical text data.
• Pre-trained Model Loading: A new function load_pre_trained_models is added to represent the process of loading pre-trained models for the retriever and generator components. This keeps the core functionalities separate. The actual loading logic (e.g., TensorFlow Hub or custom model loading) needs to be implemented based on your chosen models.

Evaluation and Future Directions:

This approach will be evaluated on benchmark medical summarization datasets to compare its effectiveness against traditional chunking methods in the RAG framework. Future work can explore:

• Fine-tuning pre-trained RAG models for the medical domain.
• Integrating domain-specific knowledge graphs to enhance the retrieval process.
• Exploring alternative summarization techniques like abstractive summarization for the medical domain.

Conclusion:

This work proposes a novel RAG-based approach for medical text summarization that leverages sectional summarization. By incorporating the inherent structure of medical documents, this approach has the potential to generate more accurate, informative, and relevant summaries for various medical applications. The use of TensorFlow allows for flexible model development and experimentation within this framework. As research progresses, this approach can contribute to advancements in automated medical information processing and improve communication within the healthcare domain.