Data Characteristics
Attenuated inactivated vaccine pharmacovigilance data originates primarily from clinical trial reports, real-world evidence (RWE), post-market surveillance systems, and individual case reports. This data typically combines structured and unstructured formats. Structured data includes fields such as patient_id, vaccine_product_code, ae_meddra_term (MedDRA coding for Adverse Events), onset_date, outcome_code, patient demographics, vaccine batch numbers, and dosage. Unstructured data consists of free-text adverse event descriptions written by clinicians or patients, medical imaging reports, and laboratory test results. Data update frequency is often high during post-market surveillance, with adverse event reports submitted in real-time. However, review and database entry usually incur delays, making weekly or monthly summary reports common. Document formats vary, including PDF reports, XML-formatted Individual Case Safety Reports (ICSRs), and database records.
Constraints on Multiturn Conversation and Prompts
The mixed structure of attenuated inactivated vaccine pharmacovigilance data challenges the accuracy of multiturn conversations. Unstructured text contains medical terminology, abbreviations, and colloquialisms, requiring robust natural language understanding for accurate information extraction. Data heterogeneity from multiple sources necessitates that the dialogue system integrates information from various origins and formats. For example, symptom descriptions extracted from clinical reports must map to MedDRA codes, impacting the efficiency of entity recognition and relationship extraction within prompts. High-frequency data streams mean the knowledge base requires frequent synchronization to ensure the dialogue model responds with the latest information, directly affecting how time-sensitive information is handled in prompts. Furthermore, the rigor of pharmacovigilance demands traceability for dialogue results, constraining how information sources and confidence levels are expressed in prompts.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Accommodates the length of adverse event descriptions and the context required for multi-turn follow-up questions. |
Chunk size (Segment Length) | 500 characters (characters) | Ensures the completeness of medical terms and symptom descriptions, preventing critical information from being truncated. |
Recall count (Recall Count) | Top 8 entries (top 8) | Covers relevant adverse event cases and medical literature from various sources. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall and precision, filtering for pharmacovigilance information highly relevant to the query. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Focuses on the most relevant adverse drug reaction reports or guidelines. |
prompt_template | includes MedDRA coding mapping instructions | Guides the model to map free-text symptoms to standard medical terminology, improving information standardization. |
Common Pitfalls
- The dialogue model fails to distinguish between normal post-vaccination reactions and adverse events requiring attention when processing common symptoms like "fever." This occurs because prompts lack clear guidance on adverse event severity levels and classification.
- When a user queries adverse reaction data for a specific vaccine batch, the system returns incomplete or outdated data. This happens because the knowledge base synchronization mechanism fails to process real-time updates from post-market surveillance data promptly.
- In multiturn conversations, the model cannot accurately link patient age, underlying diseases, and other information mentioned in different turns. This leads to inconsistent assessments of adverse event risk factors because prompts do not effectively guide the model in cross-turn entity linking and context preservation.
How to Verify Configuration
- Test the model with typical adverse event descriptions to confirm accurate identification of key symptoms, vaccine products, and adverse event onset times. Compare the results against standard information in the knowledge base, ensuring consistency with human judgment.
- Simulate a series of queries regarding adverse reactions to a specific attenuated inactivated vaccine batch. Verify that the data returned by the model aligns with the content of the latest pharmacovigilance reports and check the accuracy of data source markings.
- Conduct multiturn dialogue tests to confirm the model's ability to correctly understand and maintain context regarding patient background, adverse event progression, and other information across different turns, ensuring conversational coherence.
Note: The values provided are common starting points. Measure them 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.