Data Characteristics
Data for clinical trial pre-screening in regulatory affairs primarily originates from public databases of major global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), drug regulatory agencies (e.g., FDA, EMA), and internal clinical research documents from pharmaceutical companies. This data updates frequently; some registries update daily, while regulatory agency data releases are irregular, based on approval progress. Document structures typically include structured fields (e.g., trial ID, drug name, indication, sponsor, trial phase, primary endpoint) and unstructured text (e.g., trial protocol summaries, inclusion/exclusion criteria descriptions, adverse event reports). Field units vary; for example, dosages are often in milligrams (mg) or micrograms (μg), time periods in days, weeks, or months, and biomarker concentrations have specific units of measurement.
Constraints on Deployment and Upgrade
High-frequency data sources require deployment solutions with efficient data capture and synchronization mechanisms to ensure the timeliness of pre-screening results. Diverse and heterogeneous data structures necessitate flexible ETL (Extract, Transform, Load) capabilities to standardize data from various formats for model training and inference. Unstructured text content, especially inclusion/exclusion criteria descriptions, demands high performance from Natural Language Processing (NLP) models, requiring more computational resources for text vectorization and semantic understanding. Specific field units and specialized terminology, such as drug dosages and biomarker thresholds, require the model to accurately identify and process this numerical information during knowledge base construction and retrieval to avoid pre-screening errors due to unit confusion. Large volumes of historical data and continuously growing new data pose challenges for storage capacity and retrieval efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large clinical trial protocol documents, preventing timeouts |
Chunk Length | 800–1200 characters | Balances semantic completeness and retrieval efficiency for complex clinical inclusion/exclusion criteria descriptions |
Retrieval Count | Top 10 | Ensures coverage of sufficient potentially relevant clinical trials, improving pre-screening accuracy |
Similarity Threshold | 0.75 | Filters out irrelevant trials while maintaining recall, reducing false positives |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates clinical trial reports with numerous charts and attachments |
maxContext | Calibrate based on actual measurements | Determines the maximum processing context length based on the specific model and data complexity, preventing truncation of critical information |
Common Pitfalls
- Application creation fails with
Error response from daemon: .... This typically indicates improper network or port mapping configuration between containers during Docker Compose deployment, preventing service startup. - Slow knowledge base search and excessive response times. This may be due to a lack of effective indexing optimization for large-scale clinical trial data or insufficient vector database resource allocation.
- After upgrading FastGPT, logging in via One API shows incorrect username/password. This often occurs when environment variables or configuration files are not correctly migrated during the upgrade, leading to lost or reset authentication information.
Verification Steps
- Validate core functionality: Upload a clinical trial protocol document containing typical inclusion/exclusion criteria. Check if it parses accurately and generates knowledge base entries.
- Inspect system logs: Review FastGPT container logs for any errors, especially those related to data fetching and file parsing.
- Perform retrieval tests: Conduct multiple knowledge base retrievals for specific disease or drug pre-screening conditions. Compare returned results with expected relevance, confirming that retrieval count and similarity scores meet requirements.
- Monitor resource usage: Observe CPU, memory, and storage resource utilization after deployment. Ensure system stability under high concurrent queries and the absence of resource bottlenecks.
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.