Data Characteristics
Pharmacovigilance data for academic promotion primarily originates from clinical trial reports, real-world evidence (RWE) data, drug labels, regulatory safety information, and academic journal articles. This data updates frequently, especially for new drugs or when new adverse event signals emerge. Document structures typically include structured clinical data (e.g., patient characteristics, medication history, MedDRA adverse event codes, event timing, outcomes) and unstructured text descriptions (e.g., physician notes, patient interview records). Beyond common fields like patient ID, drug name, dosage, and administration, specialized fields include adverse event severity, causality assessment, and report source. Units involve dosage units (mg, g, IU), frequency units (times/day, days), and time units (years, months, weeks, days).
Constraints on Citation and Traceability
The multi-source nature and high update frequency of pharmacovigilance data require citation systems to rapidly integrate and index information from diverse channels. The coexistence of structured and unstructured data necessitates precise matching of standard terminology like MedDRA codes and semantic understanding of free-text descriptions during knowledge base construction. This directly impacts RAG (Retrieval Augmented Generation) systems. When citing sources, the system must accurately locate specific data points or report segments, and integrate descriptions of the same adverse event from different data sources. Furthermore, source authority is crucial for academic promotion. The system must clearly identify the original provenance of cited content, including publication name, issuing body, version number, or date, to meet compliance requirements and enhance persuasiveness. Specialized terms and abbreviations in the data also require correct parsing by the citation mechanism.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 500–800 characters | Balances completeness of adverse event descriptions with retrieval precision, avoiding excessive truncation of critical information. |
Recall Count | Top 5–8 entries | Pharmacovigilance information often requires multi-dimensional cross-verification; increasing recall count helps cover relevant evidence comprehensively. |
Similarity Threshold | 0.75–0.85 | Ensures high relevance of recalled content, reduces unnecessary noise, and improves citation accuracy. |
Rerank Return Count | Top 3 entries | In academic promotion, core evidence typically concentrates in a few highly relevant documents; streamlining reranked results highlights key points. |
maxContext | 3000–4000 characters | Pharmacovigilance reports often contain detailed clinical backgrounds and adverse reaction descriptions, requiring a larger context window for understanding. |
Citation Return Type | Paragraph and Original Link | Ensures users can directly navigate to original reports or literature to verify information accuracy and authority. |
Common Pitfalls
- Observation: The AI answer mentions an adverse reaction but fails to provide a specific report source or literature reference. Reason: During knowledge base data import, metadata from original documents (e.g., publication ID, report number, URL) was not correctly associated with chunked content.
- Observation: The AI provides adverse reaction incidence data for a drug that contradicts the latest data from regulatory agencies. Reason: The knowledge base did not timely update with the latest safety information from regulatory agencies, or the update strategy did not effectively cover all data sources.
- Observation: A user asks about "cardiac toxicity of a certain drug," and the AI returns numerous irrelevant or vague citations, failing to pinpoint key information. Reason: The knowledge base's vector embedding model did not fully understand the semantics of medical terminology and adverse reaction descriptions, leading to generalized retrieval results.
Verification Steps
- Randomly select 5-10 typical questions about specific drug adverse reactions. Check if the sources cited in the AI's answers are clear and traceable to specific literature or report
URLs. - Verify if key data cited in the AI's answers (e.g., adverse event incidence, severity) is consistent with the original literature. The allowed error range should be within specific business guidelines.
- Test the system's responsiveness to recently published new adverse reaction signals. Check if the AI can cite the latest regulatory announcements or academic research after knowledge base updates.
- When citing multiple sources, verify if the system correctly distinguishes and identifies the independence of each source, for example, through
source_idordocument_idfields.
The values provided are common starting points and 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.