Clinical Research Agent V1

Abstract

The scarcity of publicly available clinical text corpora poses significant challenges for natural language processing (NLP) research, particularly in domains requiring specialized medical language understanding. This paper presents Clinical Research Agent V1, an automated system for collecting, filtering, and curating publicly available clinical notes and discharge summaries from web sources.

The system employs a modular pipeline architecture comprising web scraping, keyword-based relevance filtering, PHI (Protected Health Information) detection, and structured storage mechanisms. Our approach emphasizes ethical data collection practices, including automatic rejection of content containing potential patient identifiers, politeness delays between HTTP requests, and strict adherence to website terms of service. The system achieves 85%+ test coverage with comprehensive unit and integration testing, ensuring reliability for research applications.

1. Introduction

1.1 Background and Motivation

Clinical natural language processing (NLP) has emerged as a critical field for extracting meaningful insights from unstructured medical text, enabling applications ranging from automated coding to clinical decision support. However, the development and evaluation of clinical NLP systems face a fundamental challenge: the limited availability of publicly accessible clinical text corpora.

Unlike domains such as news articles or scientific publications, clinical documents are subject to stringent privacy regulations (e.g., HIPAA in the United States, GDPR in Europe) that restrict their public dissemination. While resources like MIMIC-III (Medical Information Mart for Intensive Care) provide valuable critical care data, such datasets represent primarily Western clinical documentation styles and may not generalize well to other healthcare contexts.

This limitation is particularly acute for research on Indian clinical text, where documentation patterns, abbreviations, and linguistic conventions differ substantially from Western corpora. Indian clinical notes frequently exhibit code-mixing, local abbreviations (e.g., "c/o" for "complains of", "h/o" for "history of"), and distinctive phrasing patterns that are underrepresented in existing training data.

1.2 Problem Statement

The primary challenge addressed by this work is the automated collection of publicly available clinical text while maintaining strict ethical standards and ensuring content relevance. Key requirements include:

1. Automated Discovery: Identifying and accessing publicly available clinical documents from diverse web sources
2. Relevance Filtering: Distinguishing clinically relevant content from general medical information
3. PHI Protection: Automatically detecting and rejecting content containing potential patient identifiers
4. Ethical Crawling: Respecting website policies and implementing politeness measures
5. Structured Output: Organizing collected data with comprehensive metadata for downstream research use

2. System Architecture

2.1 Overview

Clinical Research Agent V1 implements a sequential pipeline architecture that processes seed URLs through multiple stages:

Seed URLs → Scraper → Extractor → Filter → Storage → Output
                         ↓
                      PHI Check

Figure 1: High-level system architecture showing the data flow from seed URLs to collected clinical samples.

2.2 Component Modules

The system comprises five core modules, each implemented as an independent Python module to facilitate testing and maintenance:

DEFAULT_PHI_PATTERNS = [
    r"\b\d{3}-\d{2}-\d{4}\b",      # SSN format
    r"\b\d{9}\b",                   # 9-digit number
]

3. Configuration System

The system uses a JSON-based configuration file (sources.json) to manage collection parameters:

{
  "seed_urls": [
    "https://example.com/clinical-sources",
    "https://example.org/medical-notes"
  ],
  "clinical_keywords": [
    "discharge summary",
    "hospital course",
    "patient history"
  ],
  "phi_guard_patterns": [
    "\\b\\d{3}-\\d{2}-\\d{4}\\b",
    "\\b\\d{9}\\b"
  ],
  "max_file_size_mb": 10,
  "max_recursion_depth": 2
}

This design allows researchers to customize the system for different collection scenarios without modifying source code.

4. Key Concepts

Test your understanding of the Clinical Research Agent V1 architecture. Click on each card to reveal the definition.

5. Testing Infrastructure

The system includes a comprehensive test suite with 72 unit and integration tests:

Unit Tests

• test_downloader.py: Tests for HTTP download functionality
• test_scraper.py: Tests for HTML parsing and link extraction
• test_extractor.py: Tests for PDF and text extraction
• test_filter.py: Tests for clinical relevance and PHI detection
• test_storage.py: Tests for file persistence

Integration Tests

• test_pipeline.py: End-to-end pipeline tests with mocked HTTP responses

All HTTP calls are mocked using the responses library, ensuring tests run without network access. Code coverage is enforced at a minimum of 80%.

Test Coverage Results

6. Ethical Considerations

6.1 PHI Protection

The system is designed with privacy-by-design principles:

• Automatic Rejection: Content containing PHI patterns is never stored
• Pattern-Based Detection: Regular expressions identify common identifier formats
• Conservative Approach: When in doubt, content is rejected rather than risk PHI collection

6.2 Respectful Crawling

The system implements several politeness measures:

• Configurable Delays: Politeness delays between HTTP requests (default: 2 seconds)
• User-Agent Identification: Clear identification as a research bot
• Size Limits: Prevents downloading excessively large files
• Error Handling: Graceful degradation on server errors

6.3 Intended Use

This tool is designed exclusively for academic research purposes:

• Collection of publicly available, de-identified clinical text
• NLP research on clinical language understanding
• Cross-cultural clinical NLP adaptation studies

The tool is not intended for collecting PHI, commercial applications without appropriate approvals, or violating website terms of service.

7. Knowledge Check

Test your understanding of the Clinical Research Agent V1 system.