Vector Models and Indexing for Pharmacovigilance

Data Characteristics in Pharmacovigilance

Pharmacovigilance data primarily originates from real-world evidence gathered post-market approval. Sources include spontaneous reporting systems (e.g., FAERS, EudraVigilance), clinical trial reports, medical literature, social media, and patient feedback. This data updates frequently, often experiencing rapid growth, especially during early market phases or when new risk signals emerge.

Document structures are diverse. They range from structured report forms to extensive unstructured text, such as case descriptions, physician notes, and patient self-reports. Fields include drug name, batch number, adverse event description, patient demographics, medication history, concomitant medications, event time, severity, and outcome. Adverse event descriptions frequently contain medical terminology, abbreviations, and multiple languages. Units cover dosage (mg, g, IU), time (days, weeks, months), and frequency.

Constraints Imposed by These Characteristics on Vector Models and Indexing

High update frequency in pharmacovigilance data demands efficient incremental update capabilities for vector indexes. This ensures timely retrieval and analysis of new reports. The diverse data sources, particularly the large volume of unstructured text, necessitate robust text preprocessing. This includes medical term recognition, abbreviation expansion, and entity extraction to improve vectorization accuracy. Multi-language support in vector models is essential, either through cross-lingual embeddings or multi-language models.

Structured fields, such as drug name and severity, must integrate effectively with unstructured text vector representations. This enables fine-grained retrieval and filtering. The complexity and detail of adverse event descriptions require segmenting strategies that preserve critical contextual information, preventing semantic loss from over-truncation. The ability to process time-series data also places higher demands on index query efficiency.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Balances context preservation and retrieval efficiency, accommodating adverse event description lengths.
`Chunk overlap` (Segment Overlap)	`50–100 characters`	Ensures semantic continuity across segments, especially at event description transitions.
`Recall count` (Recall Count)	`10–20 entries`	Guarantees sufficient coverage for initial recall, providing enough candidates for subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	Calibrate based on measurements	Adjusts based on specific business requirements for recall precision and recall rate.
`Rerank result count` (Re-rank Return Count)	`3–5 entries`	Focuses on the most relevant results, reducing the manual screening burden on engineers.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses the parsing needs of large or complex case reports, preventing timeouts.

Common Pitfalls

During knowledge base import, the system reports file parsing failure or empty content. This can occur when uploading non-standard PDF or image files, leading to text extraction errors, or if the file size exceeds the UPLOAD_FILE_MAX_SIZE limit.
Retrieval results contain numerous irrelevant or low-quality snippets. This usually results from an improper segmentation strategy, such as a Chunk size that is too short leading to context loss, or a Similarity threshold set too low, recalling excessive noise.
The system responds slowly or experiences out-of-memory errors when importing large datasets. This might relate to incorrect vector database configuration (e.g., PostgreSQL work_mem) or excessively high concurrency during batch imports.

Validation Steps

Upload typical adverse event report documents (one structured, one unstructured). Check if segments in the knowledge base are reasonable and if critical information is fully retained.
For known adverse event cases, try different query statements. Observe the relevance and ranking of recall results. Evaluate if Recall count and Rerank result count meet requirements.
In a simulated high-concurrency import scenario, monitor system resource utilization (CPU, memory) and index update speed. Ensure system stability and assess if PARSE_FILE_TIMEOUT_SECONDS is sufficient.
Compare retrieval results at different Similarity threshold values. Combine this with feedback from business experts to determine a threshold range that effectively balances precision and recall.

The values provided are common starting points and should be measured 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.