GenAI-Powered Cancer Pain Management with On-premise SLMs, Knowledge Graphs, and Ontologies

Abstract

The growing volume of research in oncology, particularly related to cancer pain management, poses a significant challenge for researchers, clinicians, and decision-makers. Literature reviews are crucial but are time-consuming and often hinder the rapid integration of new findings into clinical practice.

This white paper presents a Gen AI-powered solution designed to automate the summarization and extraction of key insights from a vast corpus of oncology research papers on cancer pain management. The core value of this solution lies in the use of Small Language Models (SLM) deployed on-premise to address critical data security concerns while maintaining efficiency in the summarization process.

The solution integrates Ontologies and Knowledge Graphs to enhance the AI’s understanding and generate highly accurate, context-rich insights, overcoming the limitations of SLMs compared to Large Language Models (LLMs).

Introduction

The growing volume of research in oncology, especially in cancer pain management, presents a significant challenge for researchers, healthcare practitioners, and decision-makers. Literature reviews, though essential, are time-consuming and often hinder the discovery of innovative strategies. This white paper explores an advanced AI-powered solution that addresses this challenge by automating the summarization and extraction of key insights from oncology research. This will ultimately improve decision-making and reduce the time spent on literature reviews.

Business Need

The sheer volume and complexity of oncology research in cancer pain management make it difficult for researchers to make informed decisions. Key challenges include:

• Time-consuming literature reviews: Manually reviewing research papers is tedious, limiting the ability to identify emerging trends or innovative strategies in pain management quickly.
• Data security: The sensitive nature of clinical research data necessitates on-premise solutions to ensure that proprietary and patient-related information is not exposed.
• Limitations of SLMs: While Small Language Models (SLMs) are more suited for deployment on-premise due to their lower computational requirements, they are not as effective as Large Language Models (LLMs) in understanding and processing complex domain-specific information.

Our solution aims to address these challenges by leveraging the strengths of SLMs in a secure, on-premise environment while incorporating Ontologies and Knowledge Graphs to improve model performance and generate high-quality insights.

GenAI-Powered Solution 

Our solution leverages SLMs fine-tuned on oncology and cancer pain management research, utilizing advanced Ontologies and Knowledge Graphs to ensure accurate, context-aware summarization and insight extraction. The system integrates the following key components:

• Small Language Model: By fine-tuning the Phi3 model—a compact, efficient small language model—on a curated dataset of oncology research papers, medical journals, and pain management data, we ensure that the system can process domain-specific language with accuracy while being lightweight enough for on-premise deployment.
• Ontologies and Knowledge Graphs: To compensate for SLMs' limitations in understanding complex medical terminologies and relationships, we introduce Ontologies and Knowledge Graphs. These structures represent the relationships between cancer types, pain management strategies, drugs, treatments, and patient outcomes, and they provide a semantic backbone that enhances the language model’s ability to generate accurate summaries and extract insights.
• Retrieval-Augmented Generation (RAG): By using RAG alongside embeddings stored in Chroma DB, we ensure that the system retrieves relevant research from a vast corpus and generates high-quality insights based on these documents, enriched by the contextual data from ontologies and knowledge graphs.

The Role of Ontologies and Knowledge Graphs

Ontologies in Cancer Pain Management

An Ontology is a formal specification of the concepts within a domain, along with the relationships between those concepts. In the context of cancer pain management, an ontology would define entities like:

• Cancer Types: Different types of cancer associated with varying pain management needs.
• Pain Management Techniques: Medications, therapies, and interventions used to alleviate pain in cancer patients.
• Patient Characteristics: Variables like age, cancer stage, and co-morbidities that influence pain management strategies.

The primary value of ontologies lies in their ability to provide a structured representation of domain knowledge, which helps the language model understand relationships and context more effectively.

Knowledge Graphs in Cancer Pain Management

A Knowledge Graph is a dynamic and scalable graph structure that connects entities through relationships. It allows the representation of both explicit knowledge (e.g., direct connections between cancer types and pain management strategies) and implicit knowledge (e.g., correlations between certain drugs and outcomes that can be inferred from the data).

The key advantage of knowledge graphs is their ability to connect diverse data points from multiple sources, enabling researchers to uncover patterns and gain insights that might otherwise be difficult to identify. In our solution, the knowledge graph provides a flexible, data-driven framework that enhances the language model’s ability to generate insights, recommendations, and summaries.

Can Ontologies and Knowledge Graphs Be Combined or Used Independently?

Both Ontologies and Knowledge Graphs serve critical roles in improving the performance of the AI system, but their application can vary depending on the use case:

• Enhanced Insight Generation: Ontologies provide structured, semantic definitions of concepts, while knowledge graphs link these concepts together dynamically and flexibly. Combining them ensures that the AI system has both conceptual clarity (via ontologies) and data-driven insights (via knowledge graphs).
• Rich, Context-Aware Summarization: Using both structures, the system can deliver more accurate and context-rich summaries and insights from research papers.

• Focused Domain Knowledge: If the goal is to create a highly specialized system that provides deep, formalized knowledge of a specific domain (e.g., oncology), ontologies alone can be sufficient. However, without a knowledge graph, the system might lack the flexibility to integrate data from diverse sources and relationships.

• Dynamic Data Integration: A knowledge graph can be highly effective when data constantly evolves and new connections between entities need to be explored. However, without the formal structure of ontologies, the model might struggle to generate insights with a deep, semantic understanding of the domain.

The ideal solution often lies in combining ontologies and knowledge graphs, allowing the system to balance semantic understanding with flexible, data-driven reasoning.

Technical Approach

Data Collection:

A large dataset of relevant research articles, medical journals, and pain management data, likely in PDF or text format, focusing on cancer and pain management.

Model Selection and Fine Tuning: Phi-3, a compact SLM, is chosen for its balance of performance and on-premise deployability. Leverage prompting techniques and, if required, employ a supervised fine-tuning approach, likely training the SLM on pairs of input text (research paper snippet) and output (its summary).

Ontology Creation:

• Concept Identification: Extract key concepts like "Cancer Types," "Pain Management Techniques," "Drugs," "Patient Characteristics," "Treatment Outcomes," etc.
• Relationship Definition: Establish relationships between these concepts (e.g., "Cancer Type is_treated_by Pain Management Technique"). We can leverage tools such as Protege to create a visual ontology.
• Example:
  

• Data Sources: Extract entities and relationships from Research Papers, Databases, and Ontologies.
• Graph Structure: Use a graph database (Neo4j) to store the entities and relationships. Utilize graph database tools for schema creation, data loading, and querying.
• Example:
  
• Knowledge Graph Population and Maintenance: Data will need to be continuously updated as new research is published and new relationships are discovered.

Retrieval-Augmented Generation (RAG): Embed all research papers and store embeddings in a vector database ChromaDB.

Generation Process Using Prompt Engineering: Create a prompt that contains user queries, retrieved documents, relevant information from ontologies, and knowledge graphs from the relevant entities for the query and how they are related. Pass the enhanced prompt to the SLM for summarization or insight generation.

Workflow with Examples

Illustrated Example:

1. User Query: "What are effective non-opioid treatments for neuropathic pain in lung cancer patients?"
2. Retrieval: The system retrieves documents related to "neuropathic pain," "lung cancer," and "non-opioid" options.
3. Relevant concepts are identified in ontology and KG: "neuropathic pain," "lung cancer," and "non-opioid analgesics" like (Gabapentin and pregabalin), and their relationships.
4. Prompt: A prompt is generated that includes extracted document snippets, mentions of "neuropathic pain," "lung cancer," and "non-opioid analgesics," and their relationships from the ontology and knowledge graph.
5. SLM Processing: The model receives this enhanced prompt.
6. Output: "Gabapentin and pregabalin have shown promise in managing neuropathic pain associated with lung cancer. Clinical trials also suggest efficacy for certain antidepressants. Non-pharmacological options like acupuncture may also provide additional relief."

Key Benefits

1. Data Security and On-Premise Deployment: The use of SLM in an on-premise environment ensures that sensitive clinical and research data is secure without the need to rely on cloud-based solutions.
2. Faster and More Accurate Research: Automating the process of summarizing research papers and extracting insights enables faster literature reviews, reducing the time researchers spend gathering and interpreting data.
3. Enhanced Contextual Understanding: By incorporating Ontologies and Knowledge Graphs, the solution provides a more context-aware understanding of cancer pain management, enabling more accurate summaries and actionable insights.
4. Cost Optimization and Resource Efficiency: Automating the literature review process with an AI-powered solution reduces the need for manual labor, thus lowering costs and improving resource efficiency in research and clinical environments.

Conclusion

The Gen AI-powered solution, which uses Small Language Models (SLM) for secure on-premise deployment, combined with Ontologies and Knowledge Graphs, revolutionizes the way researchers and healthcare practitioners interact with oncology research on cancer pain management.

The integration of ontologies and knowledge graphs enhances the model's performance and ensures that the insights generated are highly relevant and contextually accurate. The solution will significantly improve decision-making, speed up research processes, and optimize costs in both research and clinical settings.