Data Characteristics in This Category
Mental illness clinical trial data is highly heterogeneous and complex. Data sources include Electronic Health Records (EHR), Patient-Reported Outcomes (PROs), scale assessments (e.g., Hamilton Depression Rating Scale HAMD, Positive and Negative Syndrome Scale PANSS), genomic data, imaging reports (MRI, fMRI), and wearable device physiological indicators. Data update frequencies vary. Scale assessments typically update periodically (e.g., weekly, monthly). Physiological indicators may transmit in real-time or near real-time. Genomic data remains relatively stable. Document structures include structured fields and extensive unstructured text, such as doctor's clinical notes and patient interview records. Field and unit specificities include scale scores, which are usually integers or specific floating-point numbers. Physiological indicators include units like frequency and amplitude. Genomic data involves biological units like base pairs and mutation sites.
Constraints Imposed by Data Characteristics on "HTTP Interface and External Systems"
Mental illness clinical trial pre-screening data characteristics impose multiple constraints on HTTP interfaces and external systems. High proportions of unstructured text data require interfaces with robust text processing and information extraction capabilities. For example, Natural Language Processing (NLP) techniques can structure key symptoms and medication history from doctor's notes. Multi-source heterogeneous data requires interface designs with good scalability and compatibility, capable of aggregating data streams from different systems. Periodically updated data, such as scale assessments, requires interfaces to support scheduled polling or event-driven push mechanisms to ensure data timeliness. Additionally, genomic data and imaging reports typically involve large data volumes, demanding high transmission efficiency and stability from interfaces. This may require support for chunked transfer or asynchronous processing. High data sensitivity requires interfaces to strictly adhere to data security and privacy protection protocols during transmission and storage, such as using HTTPS for encrypted transmission.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Clinical notes and scale assessments for mental illness are rich in text content; sufficient context processing capability is necessary. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large unstructured texts (e.g., lengthy medical records) and complex reports can take a long time. |
chunkSize | 800–1200 characters | Mental illness symptom descriptions are detailed; longer segment lengths maintain semantic integrity. |
maxRetrieve | top 15 items | Pre-screening requires recalling relevant data from multiple dimensions; increasing recall count improves coverage. |
HTTP_REQUEST_TIMEOUT_SECONDS | 120 seconds | External system data sources may respond slowly due to large data volumes or network conditions; sufficient timeout is reserved. |
API_KEY_HEADER_NAME | Authorization | Most external systems use this standard HTTP header for authentication, ensuring consistency. |
Common Pitfalls
- API request fails, returns
404 - Resource Not Found: This usually occurs due to incorrect external system interface URL configuration or a mismatch between the request path and the actual resource. - Knowledge base citation data is empty or incomplete: The HTTP response data structure does not match the knowledge base's expected format, preventing correct parsing or mapping of key fields.
- Pre-screening results do not reflect data updates in a timely manner: The external system data synchronization mechanism is not configured correctly, such as not setting up scheduled polling tasks or not handling push events, leading FastGPT to retrieve old data.
How to Verify Configuration
- Send test requests to the configured HTTP interface. Check if the external system returns the expected status code (e.g.,
200 OK). - Check if data obtained from the HTTP interface successfully imported into the knowledge base. Verify that key field content matches the original data.
- Monitor system logs. Confirm that HTTP interface calls execute as planned during data update cycles and show no significant errors.
- Use FastGPT's debugging interface. Verify that data returned by the external system is correctly parsed and structured, and usable for subsequent pre-screening processes.
The values provided 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.