Data Characteristics
Infectious disease pharmacovigilance data originates from clinical trial reports, real-world observations, spontaneous patient reports, and professional literature. Data updates occur frequently, especially during new infectious disease outbreaks, with daily or even hourly increments. Document structures are diverse, including structured Case Report Forms (CRFs), semi-structured patient follow-up records, and unstructured medical texts (e.g., handwritten doctor's notes, patient descriptions). Beyond standard patient demographics, medication history, and adverse event descriptions, specific fields include pathogen information, infection site, disease severity scores (e.g., SOFA score), treatment plan adjustments, and antimicrobial susceptibility results. Units for dosage often involve mg/kg or mL/hour, time units are precise to hours or days, and laboratory test results have specific measurement units.
Constraints from "HTTP API and External Systems"
The high frequency of infectious disease data updates requires HTTP APIs with high throughput and low latency to support real-time or near real-time information synchronization. The large volume of unstructured text means standard structured data interfaces are insufficient for efficient processing, necessitating text parsing and information extraction services. Unique fields like pathogens and infection sites require external system integration to ensure accurate data model mapping, preventing information loss or misinterpretation. The complex unit system demands that interfaces explicitly identify units during data transfer or provide unit conversion mechanisms. Furthermore, sensitive patient privacy information (e.g., HIV infection status) must use HTTPS for HTTP transmission, and data anonymization should be considered. During sudden epidemics, external systems may need rapid scaling to handle data surges, placing higher demands on API stability and scalability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxConcurrentRequests | Calibrate based on actual measurements, e.g., 20-50 concurrent requests | Infectious disease data updates rapidly, requiring higher concurrent processing capabilities while avoiding overload. |
requestTimeoutSeconds | 30 seconds | Ensures timely API response and prevents data delays due to long waits. |
chunkSize | 500-800 characters | A moderate chunk length for abundant unstructured text improves embedding and retrieval quality. |
maxRetryAttempts | 3 times | Provides a limited retry mechanism to enhance robustness during network fluctuations or transient external system failures. |
securityProtocol | HTTPS | Mandatory encryption for transmitting sensitive patient information to ensure data security. |
dataValidationSchema | Strictly define enumerations or formats for unique fields like pathogens and infection sites | Ensures data quality and consistency for specific infectious disease fields. |
Common Pitfalls
- Symptom: Pathogen or infection site fields are empty in adverse event reports received by the external system. Reason: The HTTP API failed to correctly identify or extract specific entities from unstructured text during data mapping.
- Symptom: The HTTP API frequently returns
503 Service Unavailableerrors when processing large-scale epidemic data. Reason: The increase in data volume was underestimated, andmaxConcurrentRequestswas set too low, leading to insufficient system processing capacity. - Symptom: Knowledge base retrieval results for specific drug dosages are inconsistent with actual values. Reason: The HTTP API transmitted dosage data without explicit units, leading to unit confusion during knowledge base parsing.
Verification Steps
- Simulate high concurrent requests to check the HTTP API's response time and error rate, ensuring stable operation under expected load.
- Randomly select multiple infectious disease reports containing unstructured descriptions, import them into the external system via the API, and verify that key fields like pathogens and infection sites are accurately extracted and mapped.
- Design drug dosage data with different units, transmit it via the API, and confirm that the external system correctly parses and retains unit information or performs appropriate conversions.
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.