Data Characteristics in this Domain
Pharmacovigilance data primarily originates from post-market drug surveillance reports, clinical trial reports, literature, and spontaneous patient reports. This data updates frequently. New drug launches, in particular, can lead to a surge in adverse event reports, requiring real-time processing. Data documents typically contain both structured and unstructured information. Structured data includes patient demographics, drug information (e.g., drug name, batch number, dosage, administration method), adverse reaction events (e.g., MedDRA codes, onset time, severity), and outcomes. Unstructured data consists of detailed clinical descriptions, physician diagnostic records, and free-text patient complaints. Fields may involve industry-specific standards such as ICD-10 disease codes and ATC drug classification codes. Units commonly used include milligrams (mg) and grams (g) for dosage, hours (h) and days (d) for time, and occurrences per day or per week for frequency.
Constraints Imposed by these Characteristics on Workflow Orchestration
High-frequency data updates necessitate real-time or near real-time data ingestion capabilities within the workflow to promptly identify potential safety signals. The coexistence of structured and unstructured data requires the workflow to integrate components for text parsing, entity recognition, and knowledge graph construction. This enables effective extraction and structuring of information from free text. An example is identifying drug names, adverse reaction symptoms, and their temporal associations from spontaneous patient reports. The use of industry-specific codes and units demands that the data transformation modules in the workflow accurately map and standardize this information. This prevents analysis errors due to inconsistent data formats. Additionally, this data often involves highly sensitive patient privacy, requiring strict adherence to data anonymization and compliance requirements in workflow design.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Ensures the AI conversation covers the complete context of adverse reaction reports, preventing information loss. |
Chunk size (Segment Length) | 500–800 characters | Balances segment granularity with semantic completeness, improving knowledge base retrieval accuracy. |
Recall count (Recall Count) | Top 5–7 entries | Balances retrieval efficiency with relevance, ensuring core information is recalled. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Filters out low-relevance documents, focusing on adverse reaction information that best matches the query. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Accounts for the potentially large amount of text in adverse reaction reports, allowing sufficient time for file parsing. |
maxRetry | 3 times | Addresses transient network fluctuations or service unavailability during external API calls (e.g., MedDRA coding service). |
Common Pitfalls
- The AI conversation module's output is not captured by subsequent components, leading to workflow interruption or data loss. This occurs due to incorrect configuration of output variables or input mapping for subsequent components.
- A "Cannot convert undefined or null to object" error during workflow execution typically indicates that a preceding module's output is empty or undefined, while a subsequent module expects a non-empty input.
- Knowledge base retrieval results are not correctly passed to HTTP request or AI conversation modules. This prevents the AI from referencing relevant knowledge, often due to data format mismatches or inconsistent parameter field names.
Verification Steps
- Simulate submitting different types of adverse reaction reports. Observe whether the workflow accurately parses key entities such as drug names, adverse reaction symptoms, and times.
- Check workflow logs to ensure all modules execute successfully without errors or warnings. Pay particular attention to data transformation and external API call steps.
- Verify that the AI conversation module's output accurately references relevant information from the knowledge base. Confirm it generates expected safety assessment recommendations based on adverse reaction reports.
- Cross-reference the final structured data. Ensure accurate mapping of industry-specific fields like MedDRA codes and ICD-10 codes, and consistent units.
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.