Data Characteristics for This Category
Attenuated inactivated vaccine pharmacovigilance data originates from clinical trial reports, real-world studies, post-market surveillance systems, and global pharmacovigilance databases. Data update frequency varies by source. Clinical trial data typically releases periodically during the study phase, while post-market surveillance data may aggregate quarterly or annually. Document structures primarily consist of structured reports and unstructured text reports, such as Case Report Forms (CRFs), Adverse Drug Reaction (ADR) reports, and Periodic Safety Update Reports (PSURs). Fields include patient basic information, vaccine batch numbers, vaccination history, adverse event descriptions (MedDRA coding), severity, outcome, and causality assessment. Units are often international standard units, such as dosage (IU, μg), time (days, weeks, months), and vital signs (mmHg, ℃).
Constraints Imposed by These Characteristics on "Workflow Orchestration"
Fine-grained management of vaccine batch information requires the workflow to support multi-source data association to trace adverse reactions to specific vaccine batches. High-frequency monitoring data requires the workflow to support timed triggers and incremental processing, ensuring timely detection of potential risk signals. The high proportion of unstructured text reports necessitates integrating Natural Language Processing (NLP) modules into the workflow. These modules extract key entities and events from free text and standardize them. Specialized fields like MedDRA coding require the workflow to call terminology services for automatic coding or to assist manual review. Additionally, patient privacy concerns mandate built-in data anonymization and access control mechanisms within the workflow. The rigor required for causality assessment means the workflow must submit both structured and unstructured information to the risk assessment module after data integration.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 Token | Accommodates longer text descriptions often found in adverse event reports. |
Recall Count | 20 | Ensures retrieval of sufficient relevant vaccine batch information and historical cases from the knowledge base. |
Similarity Threshold | 0.78 | Balances recall precision and coverage, filtering out irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing requirements for large safety report files. |
Chunk Length | 800 characters | Optimizes long text chunking, improving text comprehension and embedding quality. |
Rerank Return Count | 5 | Selects the most relevant few pieces of information to assist final decision-making. |
Three Common Pitfalls
- HTTP request node output not displayed in the conversation: This occurs when the workflow's output configuration does not explicitly map the node's
outputfield to the conversation response, leading to information being discarded. - Database query SQL generation errors or execution failures: Typically, the AI model fails to accurately understand business intent and database table structure, or generates SQL statements incompatible with specific database dialects.
- Timeout when processing a large number of adverse event reports in a loop: This happens when internal operations within the loop (e.g., file parsing, NLP processing) take too long and lack batch processing optimization, causing individual workflow execution to exceed platform limits.
How to Verify Correct Configuration
- Simulate submitting test data containing vaccine batches and adverse event descriptions. Check if the workflow accurately links to the correct knowledge base entries and outputs relevant batch information.
- For typical unstructured adverse event reports, verify if the NLP module in the workflow correctly extracts key entities (e.g., drug names, adverse reactions, dosages) and performs standardized encoding. Observe the completeness of the output fields.
- Review workflow logs to confirm all nodes (especially HTTP requests, database operations) return successful status codes and no unexpected errors or warnings appear.
- After a workflow test run, compare the output results with expected results. Pay particular attention to complex logic branches and data transformation stages to verify the correctness of data flow and processing logic.
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.