BIJIB

Data Sources:

֍ LLM-Based Document Processing: Utilize tools like LangChain to extract and preprocess text from uploaded documents.
֍ RESTful APIs: Use FastAPI for submitting UI data.
֍ Unstructured Document Loader: Support loading diverse file types, including text files, PowerPoint presentations, HTML, PDFs, images, and more.

Risk Analysis

Disease Progression Analysis Agent System:

֍ LLMs for Classification: Use large language models to classify disease progression based on historical and real-time data.

Agent Systems:

      ֍ Employ Llama-3.3-70B (open source) for analyzing text-based patient disease details.
      ֍ Use vision models like Llama-3.2-11B-Vision for analyzing images such as X-rays.

֍ Rule-Based Recommendations: Develop stage-specific rules and recommendations using LangChain’s prompt engineering.

Personalized Treatment Plan

Tools:

֍ Multi-Agent Systems: Utilize frameworks like       CrewAI, AutoGen, and LangGraph.
֍ LLM Framework: LangChain.
֍ Large Language Models: Open-source   Llama-3. 3-70B and paid GPT4o.
֍Vector Databases: Milvus and Qdrant.

Recommendation Agent:

֍ Analyzes medical records, health monitors, prior surgeries, allergies, and prolonged diseases to generate recommendations.
֍ Automatically retrieves data from connected sources.

Personalized Treatment Plan Agent:

֍ Collaborates with the Recommendation Agent to deliver tailored treatment plans.
֍ Integrates data sources and agent recommendations to refine the output.

Clinical Decision Support

Tools:

֍ AI Agents: Frameworks such as CrewAI, AutoGen, and LangGraph.
֍ Large Language Models: Open-source Llama-3.3-70B and paid GPT4o.
֍ Vector Databases: Milvus and Qdrant.

Agent Functions:

֍ EHR Data Analysis: Continuously ingests and analyzes electronic health records (EHR) to identify high-risk trends (e.g., abnormal vital signs, lab results, or patient history). Insights are stored in vector databases for rapid access.
֍ Prioritization: Assigns urgency levels to cases using predictive models, ensuring clinicians focus on the most critical patients.
֍ Real-Time Recommendations: Provides actionable insights, such as medication adjustments or additional tests, based on continuous monitoring. The vector database enables contextually relevant data retrieval.

Document Sources:

֍ IoT Devices
֍ Documents