Data Characteristics in This Category
Infectious disease pharmacovigilance data originates from clinical trial reports, real-world observations, case reports, literature reviews, and regulatory databases. This data updates frequently, potentially hourly or daily, especially during outbreaks or early stages of new drug launches. Data document structures vary and are complex, including free-text descriptions, structured fields, and semi-structured medical terminology reports. Key fields include patient demographics, infection type (e.g., bacterial, viral, fungal), pathogen identification results, medication history, adverse event timing and severity, treatment measures, and outcomes. Data units typically include dosage units (mg, IU), time units (hours, days), and severity scores (e.g., CTCAE grades).
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The multi-source nature and high update frequency of infectious disease pharmacovigilance data require workflows with flexible data ingestion and real-time processing capabilities. Document structure complexity necessitates advanced text parsing and entity recognition modules to accurately extract key information from free text, such as pathogen names, antibiotic types, and resistance data. The presence of various fields and units demands fine-tuned configuration in data standardization and normalization stages to ensure correct merging and analysis of data from different sources. Additionally, the rapid evolution of infectious diseases requires highly efficient knowledge base update mechanisms within the workflow to reflect the latest disease guidelines, resistance pattern changes, and drug interaction information.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 | Accommodates free-text report length while balancing processing efficiency and cost. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness and recall accuracy, preventing information fragmentation. |
Recall count (Recall Count) | Top 8 | Ensures coverage of relevant knowledge points while reducing unnecessary computational overhead. |
Similarity threshold (Similarity Threshold) | 0.78 | Addresses the precise matching requirements for medical terminology, reducing false positives. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the parsing time for large clinical reports and literature. |
knowledgeBaseUpdateInterval | Calibrate based on measurement, e.g., every 24 hours | Addresses the update frequency of infectious disease knowledge, ensuring information timeliness. |
Three Common Mistakes
- Tool calling modules fail to execute external API calls correctly. Workflow execution logs show HTTP status codes 4xx or 5xx. This occurs due to expired API credentials or request parameter formats that do not conform to API specifications.
- Knowledge base query results are empty or irrelevant. The AI assistant's responses lack critical medical facts or provide generic descriptions. This happens when the knowledge base index is not updated promptly, or the
Similarity threshold(Similarity Threshold) for queries is set too high, filtering out relevant documents. - Code execution modules within the workflow report errors. Logs show
ModuleNotFoundError. This indicates missing specific Python library dependencies in the FastGPT container environment, requiring additional installation.
How to Confirm Correct Configuration
- Upload a typical new infectious disease case report. Observe if the workflow accurately extracts key entities, such as pathogen names, antibiotic usage, and adverse event types. Compare results with manual annotations to determine entity recognition accuracy.
- Update the knowledge base with a batch of the latest resistance patterns or drug interaction information. Then, query through the workflow to verify if the AI assistant references the most current information. Compare with older data to confirm knowledge update timeliness.
- Execute simulated batch data processing tasks. Monitor workflow execution time and resource consumption. Ensure performance remains as expected with increased data volume. Set an upper limit for processing latency based on business requirements.
The values given 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.