Data Characteristics
II-III clinical trial data originates primarily from Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC) systems, and Pharmacovigilance (PV) systems. Data updates are frequent, especially during trials, with EDC systems generating new Case Report Form (CRF) data almost in real-time. These data documents have complex structures, including modules for subject recruitment, randomization, drug administration, efficacy evaluation, and safety event recording. Field types are diverse, covering numerical values (e.g., dosage, biomarker levels), text (e.g., adverse event descriptions, medical terminology), dates (e.g., visit dates, event occurrence dates), and enumerations (e.g., gender, race). Units are highly standardized; for example, dosages are typically in milligrams (mg) or micrograms (μg), and time points are in days or weeks.
Constraints on Tool Calling and Plugins
High-frequency clinical data updates require tool calling to have efficient data synchronization and processing capabilities. This prevents outdated analysis results due to data delays. The complex document structures and diverse field types make it difficult for general parsers to handle directly, necessitating customized data extraction and structuring plugins. For example, accurately identifying medical terms and event severity from free-text adverse event descriptions requires deep integration of Natural Language Processing (NLP) tools. Standardized units facilitate data integration but still require attention to unit conversion or consistency checks during tool calls to prevent confusion. Furthermore, the large volume of clinical trial data places high demands on the concurrent processing capabilities and resource consumption of tools, requiring optimization of plugin execution efficiency and memory usage.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures the ability to upload structured datasets or large raw reports containing multi-center data. |
maxContext | 8000 tokens | Covers a complete case report for a single subject or key study protocol details for context understanding. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time to process large PDFs or complex tabular files, preventing parsing interruptions. |
Chunk size (Segment Length) | 1000 characters | Balances textual semantic completeness with recall efficiency, suitable for paragraph structures in clinical reports. |
Recall count (Recall Count) | Top 10 | Increases coverage of relevant information, helping to capture key evidence from a large volume of clinical documents. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement | Adjusts based on specific clinical terminology and expression habits to ensure the precision and recall rate of retrieval results. |
Common Pitfalls
- Tool call returns empty API response data. This can occur if the data source interface authentication fails or data query parameters do not match, preventing the backend service from retrieving data.
- Plugin execution times out. This is common when processing large amounts of raw clinical data or performing complex statistical analyses without adequately considering data scale and computational complexity, leading to exceeding the preset execution time.
- The AI platform provides conclusions that do not match expectations when processing clinical reports. This may be due to outdated terminology definitions or guideline versions in the knowledge base for specific diseases or drugs, or because the RAG-recalled documents do not cover all relevant information.
Verification Steps
- Call test interfaces and check for a
200status code to confirm that tools and plugins respond correctly and that the returned data structure meets expectations. - Upload typical clinical study protocols or case report forms to verify that the AI platform can accurately extract key information, such as investigational drugs, indications, and primary endpoints. Cross-reference results with manual checks.
- Simulate requests with varying data volumes and complexity. Monitor tool and plugin execution times and resource consumption to assess stability and performance under high load, and compare against baseline data.
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.