HTTP Interface and External Systems for Hemato-Oncology

Data Characteristics for This Category

Hemato-oncology pharmacovigilance data typically includes patient reports, healthcare professional reports, company-initiated reports, and literature search results. Data sources are diverse: national adverse drug reaction monitoring center databases, international drug regulatory agency databases (e.g., FDA Adverse Event Reporting System, FAERS), and clinical trial reports. Data update frequencies vary; FAERS usually updates quarterly, while internal company reports might be submitted in real-time. Data document structures are complex. They often contain patient demographics, drug usage, adverse event descriptions (using medical terminology and codes like MedDRA), event severity, outcomes, and causality assessments. Fields are mostly categorical variables and text descriptions. Some fields require natural language processing to extract structured information, such as adverse event onset dates, durations, and treatment measures.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The multi-source and heterogeneous nature of hemato-oncology pharmacovigilance data requires HTTP interfaces to have high compatibility and flexibility. This adapts to different data formats like JSON, XML, or CSV. Varying data update frequencies mean interface calls need a mechanism combining scheduled tasks and real-time triggers to ensure data timeliness. Complex document structures, especially free-text descriptions of adverse events, challenge the interface's data preprocessing capabilities. This requires preProcessScript parameters for cleaning and standardization. The presence of medical terminology and codes (e.g., MedDRA LLT_CODE, PT_CODE) means interfaces must preserve these codes during transmission and processing for subsequent professional analysis. This also requires strict character encoding, typically UTF-8. The nature of categorical variables and text descriptions affects data transmission volume and processing load, potentially requiring batch transmission or compressed processing.

Configuration Settings

Configuration Item	Recommended Approach	Rationale for This Approach
`externalApiUrl`	`https://api.fda.gov/drug/event.json` or a privately deployed API address	FDA FAERS is a public and authoritative data source; private APIs are for internal enterprise data integration.
`requestMethod`	`GET` or `POST`	Choose based on the external system's API specification; FAERS queries typically use `GET`.
`requestHeaders`	`Content-Type: application/json`	Ensures correct parsing of JSON data, or set according to the target API requirements.
`requestTimeout`	`60000` milliseconds	Accounts for external API response times and data volume, preventing task failures due to long waits.
`preProcessScript`	Calibrate based on actual measurements	Used to clean adverse event description text and extract key date fields, e.g., standardizing date strings to `YYYY-MM-DD` format.
`maxConnections`	`5`	Balances data acquisition efficiency with external system load, avoiding rate limiting from frequent requests.

Three Common Mistakes

Error Symptom: HTTP interface call returns HTTP 429 Too Many Requests error. Reason: Incorrect configuration of request intervals or concurrent connections leads to too many requests to the external system in a short period, triggering rate limiting.
Error Symptom: Adverse event description fields (e.g., patient.reaction.reactionmeddrapt) appear empty or garbled in FastGPT. Reason: The external system's data encoding does not match FastGPT's expected encoding, or preProcessScript does not correctly handle special characters or MedDRA codes.
Error Symptom: After calling an external API in the workflow, the returned JSON data cannot be parsed correctly, causing subsequent steps to fail. Reason: The API's returned JSON structure does not match the predefined data model, or the preProcessScript inappropriately modified the returned data.

How to Confirm Correct Configuration

Use FastGPT's debugging tools to perform a complete call test on the configured HTTP interface. Check if the returned data's status_code is 200 and verify if the raw returned data meets expectations.
Check if key fields in the imported data, such as LLT_CODE, PT_CODE, and adverse event description text, are complete and not garbled. Ensure correct recognition of medical terminology.
Call the interface for data from different sources. Compare data update statuses at different times. Confirm that preProcessScript correctly processes various data formats.

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.