Data Characteristics for This Category
Data for clinical trial pre-screening on pharmaceutical e-commerce platforms primarily originates from pharmaceutical companies' recruitment information, user purchase and health consultation records, drug inserts, and user medication feedback. Data updates frequently, with new clinical trials and drug information continuously published and user behavior data generated in real-time. Document structures typically include structured trial metadata (e.g., investigational drug, indications, inclusion criteria, exclusion criteria, research centers) and unstructured text descriptions (e.g., detailed recruitment narratives, patient stories). Common field units include age (years), weight (kilograms), disease duration (months/years), and dosage (milligrams/milliliters), often containing specialized medical terminology and abbreviations.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The coexistence of structured and unstructured data in pharmaceutical e-commerce demands robust parsing capabilities from tool calls. For example, extracting key inclusion/exclusion criteria from recruitment narratives requires precise identification by natural language processing tools. High-frequency data updates mean tool call results must reflect the latest information in real-time or near real-time, necessitating carefully designed caching strategies and data synchronization mechanisms. The extensive use of medical terminology and abbreviations requires underlying models for tool calls to possess medical domain knowledge to avoid inaccurate pre-screening results due to misinterpretations. Diverse field units require tools to standardize or convert units during data processing to support effective comparison and calculation across different data sources.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
tool_timeout_seconds | 60 seconds | Most external APIs respond within seconds to tens of seconds; this allows sufficient time for complex queries. |
max_tokens_per_call | 2000 | Ensures complete transmission of longer texts like clinical trial descriptions or user health reports. |
api_retry_attempts | 3 | Handles network fluctuations or temporary external API failures, improving call success rates. |
cache_ttl_minutes | 10 minutes | Balances data freshness with system load for frequently updated but not immediate data. |
medical_ontology_plugin | Enabled | Enhances understanding and matching capabilities for medical terminology and disease codes. |
data_standardization_rules | Calibrate based on actual measurements | Addresses unit and format differences across various data sources to ensure data consistency. |
Three Common Mistakes
- Symptom: Custom code calls return
AggregateError Code:ETIMEDOUT. Reason: The external API response time exceeds the system's configured timeout limit. - Symptom: In pre-screening results, some critical inclusion/exclusion criteria are not identified or are misidentified. Reason: The model's understanding of medical terminology or complex sentence structures is insufficient, or medical domain plugins are not fully utilized.
- Symptom: The MCP (Medicine Clinical Protocol) function deployed in FastGPT fails to correctly parse clinical trial protocols. Reason: The MCP plugin does not match the current data format or field mapping, or necessary parameters are not configured.
How to Confirm Correct Configuration
- For multiple typical clinical trial recruitment texts, verify that tool calls accurately extract all inclusion and exclusion criteria. Compare these with human review results to determine recall and accuracy.
- Simulate user submission of health information with different units (e.g., weight in kilograms/pounds). Verify that tool calls correctly perform unit conversion and standardization when processing this information.
- By calling external clinical trial database APIs, verify that tools successfully initiate requests and retrieve data. Check the completeness and expected structure of the returned data.
- Monitor tool call logs for frequent timeouts or API errors. Adjust
tool_timeout_secondsorapi_retry_attemptsconfigurations based on error types.
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.