Document Parsing and Chunking for Clinical Trial

Data Characteristics in This Category

Pharmaceutical e-commerce platforms handle documents from pharmaceutical companies, CROs, and regulatory bodies for clinical trial pre-screening. These documents include clinical trial protocols, investigator brochures, informed consent forms, ethics approvals, subject recruitment criteria, and historical patient data. Data sources are diverse, and updates are frequent, especially during ongoing clinical trials where protocol amendments and safety reports are common. Document structures primarily use PDF and Word formats, containing extensive unstructured text, complex tables (e.g., dose adjustment tables, adverse event classifications), images, and scanned documents. Fields and units involve medical terminology, drug names, dosage units (mg, μg, mL), time units (days, weeks, months), and laboratory indicator units (mmol/L, U/L). Abbreviations and specific coding systems are also common.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

Clinical trial documents processed by pharmaceutical e-commerce platforms have broad data sources and frequent updates. This requires document parsers to efficiently handle diverse document types and adapt to frequent incremental updates. The mix of complex table structures, unstructured text, and scanned documents challenges the parser's table recognition accuracy, OCR capabilities, and semantic text understanding. The extensive use of medical terminology, abbreviations, and specific measurement units can render traditional word segmentation and entity recognition ineffective, necessitating more specialized language model support. Furthermore, clinical trial documents are often lengthy and contain sensitive information. This imposes strict requirements on document chunking granularity, context preservation, and security to ensure accurate matching of subject conditions during pre-screening while preventing information leakage.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial protocols and investigator brochures can contain many charts, figures, and attachments, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDFs or documents with complex tables can be time-consuming; this prevents parsing timeouts.
`Chunk size`	`800–1200 characters`	Balances contextual completeness and recall efficiency, accommodating long sentences and paragraphs in medical documents.
`Chunk Overlap Length`	`150 characters`	Ensures semantic continuity between chunks and prevents critical information from being truncated at chunk boundaries.
`OCR_ENABLE`	`true`	Processes scanned documents or text in images, ensuring all content is parsable.
`TABLE_PARSE_MODE`	`Advanced mode`	Accurately identifies and extracts complex dose tables and adverse event tables in clinical trial documents.

Common Pitfalls

When uploading large PDF files, the system displays timeout of XXXms exceeded. This usually indicates that PARSE_FILE_TIMEOUT_SECONDS is set too low, and the system fails to complete parsing complex or large documents in time.
Some table content in the knowledge base cannot be retrieved or is incompletely chunked. This often results from an improper TABLE_PARSE_MODE configuration, failing to effectively recognize complex table structures in the document.
Uploaded documents contain medical terminology abbreviations, leading to inaccurate pre-screening results. This suggests that the document parser or chunking strategy does not adequately consider the linguistic characteristics of the specialized domain. It may require adjusting the word segmentation model or adding a domain-specific dictionary.

How to Verify Correct Configuration

Select a clinical trial protocol containing complex tables and scanned pages. Upload it and verify that all text and table content are correctly extracted in the knowledge base.
Randomly select key medical terms, dosage information, or recruitment criteria from the document. Use the knowledge base Q&A or retrieval function to verify if they can be accurately recalled.
Upload a document known to contain sensitive information. Check if the chunking results adhere to predefined privacy protection or anonymization rules.
Monitor backend logs to confirm that no frequent parsing failures or timeout errors occur when processing various document types.

The values provided are common starting points and should be measured against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.