Monoclonal Antibody Pharmacovigilance: Document Parsing and

Data Characteristics

Monoclonal antibody pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE) studies, post-market adverse event reports (ADRs), medical literature, and regulatory safety updates. This data typically exists as PDF clinical study reports, Word documents, structured or semi-structured text reports (e.g., CIOMS I forms, MedWatch forms), and database export files. Document structures are complex, containing extensive medical terminology, experimental data, patient characteristics, adverse event descriptions, dosage information, and treatment outcomes. Data updates frequently, especially during initial drug launches and periods requiring regular reports from regulatory bodies. Fields and units are highly specialized, including CTCAE (Common Terminology Criteria for Adverse Events) grading for adverse events, drug dosage units (mg/kg), time units (days, weeks, months), and biomarker concentrations.

Constraints Imposed by These Characteristics on "Document Parsing and Chunking"

The complexity of monoclonal antibody pharmacovigilance data places specific demands on document parsing and chunking. First, diverse document formats and semi-structured content require parsers to handle tables within PDFs, text embedded in images, and complex layouts in Word documents, accurately extracting key information. Second, highly specialized medical terminology and abbreviations render traditional keyword-matching chunking methods ineffective, necessitating greater reliance on contextual understanding and semantic relationships. Third, adverse event reports often contain lengthy narrative texts describing event progression, treatment measures, and outcomes. Chunking must preserve the integrity of individual adverse events to prevent critical information from being split. Finally, frequent data updates require the parsing and chunking process to be highly efficient and scalable to accommodate growing data volumes. Accurate identification and extraction of critical numerical values such as dosage, time, and grading are prerequisites for effective subsequent analysis.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Balances semantic completeness and recall efficiency, preventing individual chunks from being too long (diluting key information) or too short (losing context).
`Chunk Overlap Length` (Chunk Overlap Length)	`50–100 characters` (characters)	Ensures contextual continuity at chunk boundaries, improving the accuracy of cross-chunk retrieval.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Most monoclonal antibody-related clinical reports are large, requiring longer parsing times.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accounts for the file size of clinical study reports and comprehensive safety reports.
`PDF_OCR_ENABLE`	`true`	Many reports contain scanned documents or text embedded in images, making OCR recognition a necessary step.
`MAX_CHUNKS_PER_FILE`	`Calibrate by actual measurement` (Calibrate based on actual measurements)	Balances index size and recall quality based on the average length and information density of reports.

Three Common Pitfalls

Uploading a PDF results in "request error" or "parsing failed" because the PARSE_FILE_TIMEOUT_SECONDS configuration is too small, preventing large clinical trial reports from completing parsing within the default time.
In knowledge base retrieval results, the complete description of an adverse event is truncated or critical numerical values are missing. This occurs when Chunk size (Chunk Length) is set too short, leading to unreasonable splitting of semantic units.
After importing a Feishu multi-dimensional document containing tables, table data is not correctly parsed or indexed. This may be because PDF_OCR_ENABLE is not enabled, or the parser has insufficient support for complex table structures. Check parsing logs.

How to Verify Configuration

Select several representative monoclonal antibody adverse event reports (PDF, Word). Upload them and check if the Parsing Status is "successful" (Success) and if corresponding chunks are generated in the knowledge base.
Perform searches for key adverse event descriptions, dosage information, and time points within the reports. Verify if the returned results contain complete and accurate contextual information, and if critical numerical values are correctly identified.
Check parsing logs to confirm no TimeoutError or ParsingError exceptions appear. Pay attention to metrics like OCR_SUCCESS_RATE.
Simulate user queries to test specific adverse reactions, drug dosages, or patient characteristics. Evaluate the relevance and completeness of the retrieved chunks.

The values given 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.