Adverse Events

An AI-powered tool to analyze clinical trial descriptions and identify potential adverse events using multiple LLM models.

Overview

This application uses LLMs to identify whether user feedback from a clinical trial is an adverse event or not.

It uses a workflow with multiple steps, where each step utilizes different models to evaluate the text for potential adverse events. Finally, it summarizes the results in a Pharmacovigilance assessment summary.

Here's a breakdown of the workflow:

1. Context Analysis: The basic model analyzes the clinical description to determine if there is any mention of an adverse event.
2. BioClin Analysis: An intermediate model performs a more detailed evaluation, considering clinical reasoning to assess the presence of adverse reactions.
3. LLM as a Judge Analysis: A specialized model acts as a judge, evaluating the feedback from the previous analyses based on predefined criteria.
4. Judge Feedback to Context Analysis: The judge provides feedback on the context analysis, which is then used to refine the initial evaluation.
5. Judge Feedback to BioClin Analysis: Similar to the previous step, the judge reviews the bio-clinical analysis and offers feedback for improvement.
6. Context Analysis - Revised: The basic model re-evaluates the clinical description, incorporating the judge's feedback to enhance its analysis.
7. BioClin Analysis - Revised: The intermediate model revisits its analysis, integrating the judge's feedback for a more accurate assessment.
8. Judge Summary: Finally, the judge summarizes the findings from all analyses, generating a comprehensive report that includes a pharmacovigilance assessment summary in JSON format.

This structured approach ensures a thorough evaluation of the clinical trial description, leveraging multiple models to enhance accuracy and reliability in identifying adverse events.

flowchart TD
    A[Context Analysis] --> B[BioClin Analysis]
    B --> C[LLM as a Judge Analysis]
    C --> D[Judge Feedback to Context Analysis]
    C --> E[Judge Feedback to BioClin Analysis]
    D --> F[Context Analysis - Revised]
    E --> G[BioClin Analysis - Revised]
    F --> H[Judge Summary]
    G --> H

Loading

This structured approach ensures a thorough evaluation of the clinical trial description, leveraging multiple models to enhance accuracy and reliability in identifying adverse events.

Screenshots

Analysis

Summary

Features

• Multi-model analysis using various LLMs (OpenAI, Anthropic, Google, Cerebras, Groq)
• Three-tier evaluation system:
  • Basic analysis for quick adverse event detection
  • Intermediate analysis with step-by-step clinical expert reasoning
  • Advanced LLM judge analysis considering 15 clinical parameters
• Sample clinical trial descriptions for testing
• Dark mode support with auto/light/dark theme options
• Form persistence for saving user preferences
• Real-time streaming responses with progressive rendering
• Mobile-responsive Bootstrap 5.3 interface

Usage

1. Select a sample clinical trial description or enter your own
2. Customize analysis settings (optional):
   • Modify prompts for each analysis tier
   • Select different LLM models for each evaluation
3. Click "Analyze" to get multi-model evaluation results
4. Review the layered analysis from basic to advanced judge evaluation

Setup

Prerequisites

• Modern web browser with JavaScript enabled
• Access to LLM Foundry API endpoints

Local Setup

1. Clone this repository:

git clone https://github.com/gramener/adverseevents.git
cd adverseevents

2. Serve the files using any static web server. For example, using Python:

python -m http.server

3. Open http://localhost:8000 in your web browser

Deployment

On Cloudflare DNS, proxy CNAME adverseevents.straive.app to gramener.github.io.

On this repository's page settings, set

• Source: Deploy from a branch
• Branch: main
• Folder: /

Technical Details

Architecture

• Frontend: Vanilla JavaScript with lit-html for rendering
• LLM Integration: Multiple model providers through LLM Foundry API
• Styling: Bootstrap 5.3.3 with dark mode support

Dependencies

• lit-html - Template rendering and DOM updates
• marked - Markdown parsing
• asyncllm - Streaming LLM responses
• Bootstrap - UI framework and styling
• Bootstrap Icons - Icon system
• FormPersistence.js - Form state management

LLM Models

Supports multiple AI models with varying costs:

• OpenAI: GPT-4 variants ($0.15-$5)
• Anthropic: Claude 3 models ($0.25-$3)
• Google: Gemini 1.5 models ($0.04-$1.25)
• Cerebras: Llama 3.1 models (Free)
• Groq: Various models including Llama 3.2, Gemma 2, Mixtral (Free)

License

MIT