Data Characteristics for This Category
Phase II-III clinical trial pre-screening involves diverse data sources. These primarily include sponsor-provided trial protocols, Investigator's Brochures (IB), Case Report Form (CRF) design documents, and patient historical data and biomarker data from Electronic Health Record (EHR) systems and Laboratory Information Management Systems (LIMS). This data exists in both structured (e.g., database records, CSV, XML) and unstructured (e.g., clinical notes in PDF and Word documents, imaging reports) formats. Data update frequency is relatively stable during the trial design phase. Once patient recruitment and follow-up begin, patient data updates incrementally on a daily or weekly basis. Document structures are complex, containing extensive medical terminology, abbreviations, and specific fields and units such as dosage (mg/kg), frequency (QD/BID), treatment duration (Weeks), and assessment criteria (RECIST standards).
Constraints on "Deployment and Upgrade" Due to These Characteristics
Complex data sources and varied file formats require FastGPT deployments to have robust file parsing capabilities. This is especially true for accurate identification and structured extraction from unstructured medical documents. High-frequency incremental updates of patient data challenge the real-time synchronization and index reconstruction efficiency of the knowledge base. This necessitates optimizing incremental update strategies to avoid performance bottlenecks from full reconstructions. The specialized nature of medical terminology and the depth of domain knowledge influence the choice of embedding models. Models with strong performance in the biomedical field are prioritized. Furthermore, strict exclusion and inclusion criteria in trial protocols demand that pre-screening functions precisely match conditions during recall and ranking, reducing misjudgment rates. The presence of multimodal data (e.g., imaging reports) also creates a potential future need for system upgrades to support multimodal embeddings.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial protocols and investigator brochures can be large, requiring sufficient upload capacity. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Medical documents have strong contextual dependencies; overly short chunks lose critical information, while overly long chunks introduce noise. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures initial coverage of more potentially relevant information under complex filtering conditions for subsequent re-ranking. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Different embedding models and data distributions have varying sensitivity to similarity; determine through small-scale testing. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Large file parsing can be time-consuming; increasing the timeout prevents parsing interruptions. |
LLM_MODEL | Deploy a fine-tuned medical domain model | Improves understanding and generation capabilities for medical terminology and clinical context. |
Three Common Mistakes
- Key medical terms or diagnostic criteria are missing from knowledge base query results. This occurs because of an improper knowledge base chunking strategy, leading to relevant information being truncated or fragmented.
- After a system upgrade, model dialogue responses show symbols related to traceability rules at the end. This may happen if certain debugging or traceability features are enabled by default in new version configurations and not disabled in the production environment.
- Pre-screening results are not updated or the latest data is not queryable for an extended period after patient data upload. This occurs because the knowledge base's incremental indexing mechanism is not correctly configured or triggered, preventing new data from being included in the retrieval scope in a timely manner.
How to Confirm Proper Configuration
- Upload a clinical trial protocol PDF document containing complex inclusion/exclusion criteria. Verify that core criteria are accurately parsed and structurally extracted.
- Use a set of virtual patient data, known to meet and not meet specific screening criteria, to query the pre-screening function. Check the accuracy and completeness of the returned results.
- Simulate an incremental patient data update scenario by uploading new patient records. Check if the knowledge base index update completes within the expected time and if the latest data is retrievable through queries.
Note: The values provided are common starting points. Measure against your own samples for optimal performance.
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.