Data Characteristics
Infectious disease pharmacovigilance data comes from clinical trial reports, real-world study reports, post-market surveillance data, individual case safety reports (ICSRs), and medical literature. These documents contain detailed patient demographics, infection types, pathogen identification, drug treatment plans, adverse event descriptions, severity assessments, outcomes, and causality judgments. Data updates frequently, especially with new infectious diseases or drug resistance variations, leading to rapid publication of related research and reports. Document structures are complex and varied, primarily in PDF and Word formats. They include extensive unstructured text, tables, charts, and images. Fields cover medical terminology, laboratory indicators, dosage units, and timestamps. Units are relatively standardized, but synonyms and abbreviations for medical terms are common.
Constraints on Document Parsing and Chunking
The complexity of infectious disease pharmacovigilance data imposes specific requirements on document parsing and chunking. High update frequency necessitates support for rapid incremental updates and version management to ensure knowledge base timeliness. Complex document structures, especially nested tables and charts, require parsers with robust layout recognition to prevent loss or misalignment of critical information. Medical terminology with synonyms and abbreviations demands more refined text preprocessing to maintain semantic integrity and consistency of chunked content. Additionally, adverse event descriptions are often long texts containing multiple events and timelines. Standard chunking strategies may sever event chains, impacting subsequent recall accuracy. Precise extraction of numerical information like dosage, time, and laboratory results also requires parsers to identify and retain unit information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances the completeness of adverse event descriptions with recall efficiency, preventing the cutting of critical event chains. |
Overlap Length | 150–200 characters | Ensures contextual continuity and reduces semantic information loss due to chunk boundaries. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the parsing time for large clinical trial reports and complex PDF documents, preventing timeout interruptions. |
UPLOAD_FILE_MAX_SIZE | 200 MB | Handles the file size of medical reports containing numerous images and tables. |
Text Understanding Model | General-purpose model | Provides good understanding of medical terminology and complex sentence structures in infectious disease pharmacovigilance. |
Index Model (Indexing Model) | Multilingual general-purpose | Accounts for medical literature potentially containing English abstracts or specialized terms, improving cross-language recall. |
Common Pitfalls
- Table data misalignment or missing in parsing results often occurs because the parser inaccurately identifies boundaries in complex nested tables.
- Adverse event descriptions are truncated, leading to incomplete event context during recall. This happens when chunk length is set too short without sufficient consideration for semantic boundaries.
- File upload failures or parsing timeouts can occur if
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSare configured too low to handle large or structurally complex medical documents.
How to Verify Configuration
- Randomly select multiple infectious disease pharmacovigilance documents from different sources and formats. Check if the parsed chunks are complete and semantically coherent, especially for adverse event descriptions and table data.
- Upload a PDF document containing complex tables and charts. Verify that the parser correctly identifies and extracts table content without misalignment or omissions.
- Upload a large file close to the
UPLOAD_FILE_MAX_SIZElimit. Observe if the parsing completes within thePARSE_FILE_TIMEOUT_SECONDSduration.
Note: The values provided are common starting points. Measure them 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.