Cardiovascular Pharmacovigilance HTTP API and External

Data Characteristics

Cardiovascular pharmacovigilance data originates from clinical trial reports, real-world studies, spontaneous adverse event reporting systems (e.g., FDA FAERS, WHO VigiBase), and medical literature. This data updates frequently; spontaneous reporting systems continuously receive and process new reports. Document structures vary, including structured Case Report Forms (CRFs), semi-structured medical texts (e.g., discharge summaries, outpatient records), and unstructured literature abstracts. In structured data, common fields include patient demographics, medication history, adverse event descriptions (MedDRA codes), event onset and outcome times, and laboratory results. Units often involve mg, g, IU for dosage, days, hours for time, mmHg for blood pressure, and beats/min for heart rate. Electrocardiogram (ECG) data is particularly relevant in the cardiovascular field; its interpretation results typically exist as text containing complex terminology.

Constraints Imposed by Data Characteristics on "HTTP API and External Systems"

The high update frequency of cardiovascular pharmacovigilance data requires HTTP APIs to have efficient incremental synchronization mechanisms. This avoids duplicate uploads and processing of historical data. Diverse document structures mean the API must support multiple data formats. For example, JSON for structured data, and XML or plain text for semi-structured and unstructured content. Data from spontaneous reporting systems, in particular, may contain a large amount of non-standardized free text. This necessitates backend preprocessing and standardization during data ingestion, which demands robust data carrying capacity and processing timeliness from the API. Accurate MedDRA code mapping is critical. When receiving adverse event descriptions, the API may need to call an external ontology service for real-time coding or validation. Cardiovascular-specific physiological parameter units, such as blood pressure and heart rate, must maintain strict consistency during data transmission. This prevents data deviations caused by unit conversion errors. Furthermore, due to data sensitivity, secure authentication and encrypted transmission (e.g., HTTPS) are mandatory requirements for the API.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`PUSH_DATA_API_TIMEOUT`	`600 seconds`	Accommodates longer processing times by external systems during bulk data uploads, preventing timeouts.
`MAX_FILE_SIZE_MB`	`50 MB`	Balances the need to upload compressed packages with multiple reports or large literature attachments with transmission efficiency.
`BATCH_CHUNK_SIZE`	`100 records`	For structured data like adverse event reports, batch processing improves system throughput and error isolation.
`CONCURRENT_REQUEST_LIMIT`	`5`	Limits concurrent requests to avoid overwhelming external reporting systems or ontology coding services.
`RETRY_STRATEGY`	Linear backoff, `3 times`	Addresses network fluctuations or momentary overload of external systems, improving data transmission robustness.
`HTTP_AUTH_TYPE`	`Bearer Token`	Provides secure API access control. Tokens can be refreshed upon expiration, aligning with industry security standards.

Three Common Mistakes

HTTP request returns a 400 status code with the message Invalid MedDRA Code: This usually indicates that the uploaded adverse event description failed validation by the external ontology coding service, or the local mapping table is outdated.
After data synchronization, blood pressure or heart rate data for some patients is empty: This might be because the external system did not include unit information during transmission, or the API failed to correctly parse fields with units like mmHg or beats/min.
After uploading a large literature PDF, it remains in "indexing" status for a long time: This can happen if PARSE_FILE_TIMEOUT_SECONDS is set too short, causing the file parsing service to time out when processing complex documents.

How to Verify Configuration

Select sample data covering different data types (structured, semi-structured, unstructured). Upload it via the HTTP API. Check if the field content in the corresponding FastGPT knowledge base is complete and accurate.
Simulate a request containing a known incorrect MedDRA code. Verify if the API correctly returns an error message and check the error logs for corresponding entries.
Upload a large file close to the MAX_FILE_SIZE_MB limit. Monitor upload progress and processing time. Ensure completion within PUSH_DATA_API_TIMEOUT and verify that the file content is searchable in the knowledge base.

The values provided are common starting points and should be measured 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.