Data Characteristics
Medical affairs clinical trial pre-screening primarily processes data from clinical study protocols, Investigator's Brochures (IB), ethics committee approvals, Informed Consent Forms (ICF), and various regulatory guidelines. These documents are typically in PDF format. They have complex structures, including numerous tables, figures, and nested section headings. Data update frequency is relatively low, primarily occurring during protocol revisions or regulatory updates. Field names and units strictly adhere to medical professional standards, such as dosage units (mg/kg), time points (days, weeks, months), and biomarker indicators.
Constraints on Document Parsing and Chunking
Complex document structures, especially nested sections and table content, require parsers to accurately identify semantic boundaries at different levels. This prevents merging unrelated paragraphs or confusing table rows with plain text. Dense medical terminology and abbreviations require chunks to maintain contextual integrity, preventing semantic fragmentation. A low update frequency means that once parsing and chunking are complete, the knowledge base is stable, but initial processing requires high quality. Strict field and unit requirements mean that chunking must pay special attention to binding numbers and units, ensuring they are understood as a whole. For example, a drug dosage like "10 mg/kg" should not be split.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances contextual completeness with vector model input limits |
Overlap Length | 100–200 characters | Ensures semantic continuity at chunk boundaries |
Parsing Strategy | Split by Title | Adapts to multi-level title structures in clinical documents, maintaining semantic integrity |
Table Processing | Structured Parsing | Accurately extracts table data, preventing information loss |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large files like clinical study protocols, preventing parsing timeouts |
Vectorization Model | text-embedding-ada-002 | Balances accuracy and generality, suitable for medical texts |
Common Pitfalls
- When uploading large PDF files, the system prompts "some chunk vectorization abnormal": This occurs when individual chunk content is too long or contains special characters, causing the vector model to fail.
- Database query results are JSON arrays, requiring manual parsing: This happens because the DB node outputs structured data by default, and a code node is needed for secondary processing to extract specific fields.
- After document parsing, critical information (e.g., drug dosage) is incorrectly split into different chunks: This occurs when the chunking strategy does not fully consider the integrity of medical terminology, failing to treat numbers and units as a single entity.
Verification
- Randomly select multiple parsed clinical documents and inspect their chunking results. Ensure each chunk is semantically complete and contextually coherent.
- For documents containing tables, verify that table content is correctly identified and structured, with no data loss or misalignment.
- Perform retrieval tests using keywords or phrases. Confirm that information containing specialized terminology and units is effectively recalled, and the number of recalled items meets expectations.
The values provided are common starting points and should be measured against the reader's 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.