Data Characteristics in This Category
Pharmacovigilance data primarily originates from clinical trial reports, real-world studies, adverse event reports (e.g., CIOMS I forms, MedWatch forms), drug labels, post-market safety update reports, and medical literature. This data often combines unstructured text (e.g., adverse event descriptions, patient medical history) and semi-structured data (e.g., drug batch numbers, adverse event codes, patient demographic information). Update frequency is high, especially during the early stages of new drug launches and when significant safety signals emerge. Document structures are complex, containing numerous specialized terms, abbreviations, and specific coding systems (e.g., MedDRA dictionary). Fields include drug names, dosages, usage, adverse reactions, reporting times, patient-specific information, past medical history, and concomitant medications. Units cover time units (days, months, years), dosage units (mg, g, IU), and frequency units (times/day, week).
Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"
The complexity of pharmacovigilance data requires multi-turn dialogue systems to accurately understand user query intent and effectively process specialized terms and abbreviations. High update frequency means the knowledge base must quickly synchronize the latest safety information to avoid providing outdated or inaccurate advice. Mixed data structures demand prompt designs that simultaneously balance precise matching of structured fields and semantic understanding of unstructured text. For example, a user might inquire about adverse reactions of a specific drug in a particular population. This requires the system to identify the drug and population and associate them with specific clinical manifestations. In multi-turn conversations, the system needs to retain context to infer information in subsequent follow-up questions, such as proactively asking about relevant dosage or duration of use after a user mentions an adverse event. Additionally, multi-turn conversations must guide users to provide necessary information to ensure the completeness of safety assessments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Pharmacovigilance queries often require a longer context to support multi-turn follow-ups and information supplementation. |
Chunk size (Segment Length) | 500-800 characters | Ensures individual knowledge segments contain sufficient information while avoiding excessive length that could lead to information overload and reduced recall efficiency. |
Recall count (Number of Retrieved Items) | Top 5-8 items | Needs to ensure enough relevant safety information is retrieved to cover potential answers for complex queries. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Considering the precision requirements for specialized terms and semantics, a higher threshold is needed to guarantee the accuracy of retrieval results. |
Rerank result count (Number of Reranked Items) | 3-5 items | Reranks retrieved results to select the most relevant segments, improving the quality of the final answer. |
temperature | 0.2-0.4 | Pharmacovigilance scenarios demand extremely high information accuracy. A low temperature reduces the model's free generation, ensuring the rigor of responses. |
Common Pitfalls
- Dialogue responses stating "no relevant information found" or providing generic answers typically indicate that the knowledge base index is not updated in time, failing to cover the latest safety reports, or that prompts do not effectively guide the model to extract key safety information from complex text.
- The system fails to maintain accurate associations with drugs, adverse events, or patient characteristics after multiple user follow-up questions. This suggests the
maxContextparameter is set too low, leading to insufficient context retention, or that prompts lack explicit instructions for referencing historical dialogue information. - When users ask about specific drug dosages or frequencies, the system returns incorrect or inconsistent numerical values. This may be because relevant numerical units in the knowledge base are not standardized, or prompts do not emphasize the precise extraction of numbers and units.
How to Confirm Proper Configuration
- Test a series of queries containing specialized terms and abbreviations. Check if the system correctly identifies them and provides accurate answers. Evaluate recall precision by comparing with expected answers.
- Design multi-turn follow-up scenarios. For example, first ask about a drug's adverse reactions, then follow up with its manifestations in specific populations (e.g., the elderly). Observe if the system maintains contextual coherence and provides relevant information.
- Input queries involving specific dosages, frequencies, and other numerical values. Verify if the system's returned values and units exactly match the original knowledge base content.
- After a knowledge base update, immediately conduct relevant queries. Confirm if the system quickly responds to the latest safety information, evaluating the timeliness of information synchronization.
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.