Knowledge Base Retrieval and Recall for Pharmacovigilance

Data Characteristics in this Domain

Pharmacovigilance clinical trial pre-screening data primarily originates from Clinical Study Protocols, Investigator's Brochures (IB), Case Report Forms (CRF), and relevant regulatory guidelines (e.g., ICH-GCP). These documents are often in PDF or Word format, containing extensive unstructured text. This text covers drug mechanisms of action, definitions and handling procedures for Adverse Events (AEs) and Serious Adverse Events (SAEs), inclusion/exclusion criteria, and subject characteristics. Data updates are frequent, especially in multi-center, multi-stage clinical trials, where protocol amendments and safety reports are regularly published. Fields and units are highly specialized, including dosage units (mg/kg), time units (days, weeks), and laboratory indicators (U/L, mmol/L), often accompanied by medical abbreviations.

Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall

The highly specialized and unstructured nature of pharmacovigilance data challenges knowledge base text segmentation strategies. A single long text block may contain multiple key information points. Overly short segments can lead to loss of context, while overly long segments can dilute core information. Frequent updates require the knowledge base to have an efficient incremental update mechanism to ensure the timeliness of retrieval results. Diverse document formats and complex tables/charts mean traditional text extraction methods may miss critical information, demanding stronger document parsing capabilities. Furthermore, highly specialized medical terminology and abbreviations require the retrieval model to understand their semantics, preventing recall failures due to vocabulary mismatches. The ability to precisely match and range-search numerical information like dosage and time is also crucial for accurate pre-screening.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Segment Length`	`800–1200 characters`	Balances contextual completeness with information density, adapting to the paragraph structure of clinical documents.
`Segment Overlap`	`200 characters`	Ensures that key information spanning across paragraphs can be effectively linked and recalled.
`Recall Count`	`Top 8–12 results`	Increases coverage of potentially relevant results, addressing the diversity of medical terminology.
`Similarity Threshold`	`0.78–0.85`	Broadens the scope appropriately to capture more potential associations while maintaining recall relevance.
`Rerank Return Count`	`5 results`	Selects the most relevant results, improving efficiency for the engineer.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing needs of large PDF documents, preventing file processing failures due to timeouts.

Three Common Mistakes

xlsx file content recognition fails during knowledge base construction, preventing question-answer pair generation. This often occurs when xlsx files have complex structures, including merged cells or images, leading to the parser failing to correctly identify data regions.
Retrieval results contain a large amount of irrelevant or low-quality content, reducing pre-screening efficiency. This happens when the Similarity Threshold is set too low, or the text segmentation strategy is unreasonable, introducing too much noise.
After a knowledge base update, some new data is not retrieved, or retrieval results still show old version information. This may be related to the incremental update mechanism of the knowledge base not being correctly triggered or index rebuilding delays.

How to Confirm Correct Configuration

Select clinical documents containing different data types (e.g., plain text, tables, chart descriptions), upload them, and observe the knowledge base indexing status to confirm all documents are successfully parsed and indexed.
For specific drug adverse events, dosage ranges, and other key information, construct typical query statements. Check if the Recall Count and Rerank Return Count return the expected highly relevant results, and evaluate their contextual completeness.
Simulate scenarios of clinical trial protocol revisions or safety report updates. Perform an incremental knowledge base update, then immediately query to verify if the retrieval results reflect the latest data version.

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.