Data Characteristics for This Category
Pharmacovigilance data for the respiratory system primarily originates from post-market adverse drug reaction (ADR) reports, clinical trial reports, academic literature, drug inserts, and regulatory safety information. This data often exists as unstructured text, semi-structured tables, and structured databases. ADR reports are frequently free-text descriptions, including patient demographics, medication history, adverse event manifestations, and outcomes. The language is diverse, often involving medical terminology, abbreviations, and colloquialisms. Clinical trial reports are more standardized, with rigorous structures and clear data fields. Drug inserts and regulatory documents are standardized texts, updated based on post-market safety monitoring results and regulatory requirements, typically quarterly or annually, with emergency updates in special cases. Fields include drug name, active ingredient, dosage form, indications, adverse reaction type, frequency, severity, and management measures. Units involve dosage (milligrams, micrograms), time (hours, days, weeks), and frequency (times/day), often including ICD-10 or MedDRA codes.
Constraints Imposed by These Characteristics on "Multi-turn Conversation and Prompts"
The prevalence of free-text and semi-structured data in respiratory system pharmacovigilance requires multi-turn dialogue systems to possess robust natural language understanding capabilities. The system must extract critical drug, adverse reaction, patient characteristics, and temporal information from complex contexts. The use of medical terminology and abbreviations demands high professionalism and vocabulary coverage in prompts, requiring built-in or dynamically loaded domain dictionaries. The cyclical nature of data updates means knowledge base content needs regular synchronization to ensure the timeliness and accuracy of dialogue results. The existence of coding systems like MedDRA requires the system to map natural language descriptions to standard codes when understanding user queries and to convert coded information into easily understandable text when generating responses. Furthermore, the ambiguity and uncertainty of adverse reaction reports necessitate that the dialogue system considers information completeness when responding, avoids over-inference, and guides users to provide more necessary details.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 8 | Respiratory system ADR reports involve diverse information, requiring a longer dialogue history to maintain contextual coherence and prevent information loss. |
Chunk size | 800–1200 characters | Adapts to the characteristic of ADR reports having longer, information-dense paragraphs, ensuring each segment contains complete semantic meaning. |
Recall count | Top 15 entries | Increases the probability of retrieving relevant information from massive ADR data, covering more potential associations. |
Similarity threshold | Calibrated by measurement 0.75–0.85 | Balances recall breadth and precision; too low introduces noise, too high may miss relevant but differently phrased information. |
Rerank result count | Top 5 entries | Performs a secondary sort based on recall, ensuring the most relevant core information is presented in the dialogue response. |
temperature | 0.3–0.5 | Ensures accuracy and consistency of generated responses, avoiding uncertainty or hallucination in the rigorous field of pharmacovigilance. |
Common Pitfalls
- Dialogue displays "No available index model detected": This typically indicates that the knowledge base index was not successfully built or the model configuration path is incorrect.
- Dialogue interface cannot return original document links: This suggests that the knowledge base configuration does not correctly link to original documents or the document storage path is invalid.
- Response content lacks professional terminology or is ambiguous: Prompts did not adequately guide the model to use MedDRA codes or relevant medical vocabulary.
How to Verify Proper Configuration
- Conduct multi-turn dialogue tests for typical respiratory system adverse reaction scenarios. Observe if the system accurately understands patient descriptions and provides relevant drug information.
- Check if dialogue responses include key pharmacovigilance fields, such as drug name, adverse reaction type, and frequency, and compare them with original data.
- After simulating data updates, verify if the system can promptly index new data and reflect the latest safety information in dialogues.
- Validate whether the system correctly identifies medical terminology and abbreviations and maps them to standardized codes or explanations.
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.