Model Integration and Configuration for Pharmacovigilance

Pharmacovigilance Data Characteristics

Pharmacovigilance (PV) regulatory data primarily originates from Marketing Authorization Holder (MAH) submissions. These include Adverse Drug Reaction (ADR) reports, Risk Management Plans (RMPs), product information leaflets, and regulatory guidelines. Documents are typically in PDF, Word, or structured database formats. ADR reports are semi-structured, containing patient demographics, drug information, ADR descriptions, management actions, and outcomes. RMPs are more complex, covering drug safety profiles, potential risks, and risk minimization measures. Regulatory documents and guidelines are mostly plain text. Update frequency depends on regulatory requirements, such as annual revisions or event-triggered updates. Fields and units in ADR reports may include dosage (mg, g), frequency (times/day), time (hours, days), and diagnostic names (ICD codes).

Constraints on Model Integration and Configuration

The diversity of PV data requires models to process various document formats, especially accurate parsing of PDF and Word documents. The semi-structured nature of ADR reports necessitates combining structured field recognition with unstructured text understanding for information extraction. The complexity of RMPs challenges the model's ability to understand long texts and correlate information. The cyclical updates of regulatory documents mean the knowledge base must support incremental updates and quickly integrate new content. For fields with units like dosage and frequency, the model must ensure unit consistency and correct numerical parsing to prevent misinterpretation due to inconsistent units. Given the strictness of pharmacovigilance, high accuracy and recall are critical. This requires precise configuration of retrieval strategies and re-ranking mechanisms.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500-800 characters`	Balances semantic completeness with vector retrieval efficiency
`Chunk overlap` (Chunk Overlap)	`50 characters`	Ensures context continuity between adjacent chunks, reducing semantic information loss
`Recall count` (Recall Count)	`8-12 entries`	Increases the probability of recalling relevant documents, covering more potential information points
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Filters out irrelevant results, ensuring precision of recalled content
`Rerank result count` (Rerank Return Count)	`3-5 entries`	Further refines recall results, focusing on the most relevant content
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large PDF/Word documents, preventing timeout failures

Common Pitfalls

Incorrect drug dosage or frequency values returned by the model: This occurs when document parsing fails to correctly identify units or numerical values, leading to model misinterpretation.
Model still references outdated regulations after updates: This happens when the knowledge base is not updated promptly, or the incremental update strategy does not effectively cover all relevant documents.
Querying a specific ADR report yields many irrelevant results: This is due to a Similarity threshold (Similarity Threshold) set too low, or Recall count (Recall Count) being too high, leading to the retrieval of low-relevance documents.

Validation Steps

Select multiple typical pharmacovigilance query cases. Compare model answers with original document content to verify information accuracy.
Test the model's ability to correctly cite the latest regulations and explain changes for recently updated regulations or guidelines.
Use queries containing specific dosage, frequency, and other numerical information. Check if the model can accurately extract and understand these values and their units.
Simulate ADR report queries of varying complexity. Evaluate the recall rate and precision of the model's results. Adjust Similarity threshold (Similarity Threshold) and Rerank result count (Rerank Return Count) based on actual needs.

Note: The values provided are common starting points. They should be measured against specific 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.