# Clinical Trial Matching System

Intelligent patient-trial matching system combining structured eligibility checks with AI-powered medical analysis.

## Key Features

- Dynamic clinical trial data fetching from clinicaltrials.gov

- Hybrid matching system (rule-based + LLM analysis)

- XML patient data processing

- Detailed eligibility explanations

- Excel/JSON output generation

## Matching Process

Trial Discovery

- Fetches actively recruiting trials via clinicaltrials.gov API

Core Matching

Performs 3-stage verification:

- Age/Gender checks

- Medical condition matching

- AI-powered exclusion analysis

Output Generation

Produces Excel/JSON files with:

- Eligible trial IDs

- Match confidence indicators

- Human-readable explanations

## Methodology Highlights

1.Two-Stage Verification

- First-pass rule-based filtering

- Secondary LLM analysis of complex criteria

2.AI Integration

- Uses GPT-3.5-turbo for:

- Free-text criteria interpretation

- Medication history analysis

- Temporal condition evaluation

## Setup & Usage

1.Install dependencies: (bash)

- pip install pandas requests openai openpyxl

2.Set OpenAI API key: (python)

- os.environ["OPENAI_API_KEY"] = "your-key-here" 

3.Run matching: (python)

- matcher.run_matching(

    input_xml="patient_data.xml",

    output_file="matches.xlsx"

  )

## Input Requirements

Patient data in structured XML format

```xml

<Patient>

  <ID>PT-001</ID>

  <Demographics>

    <DOB>1980-05-15</DOB>

    <Gender>Male</Gender>

  </Demographics>

  <MedicalConditions>

    <Condition>Diabetes Type II</Condition>

  </MedicalConditions>

</Patient>