Data Characteristics
Bispecific antibody (BsAb) clinical trial pre-screening data originates from clinical study protocols, subject screening logs, pathology reports, imaging reports, genetic testing results, and prior treatment history. This data often combines unstructured text (e.g., handwritten clinician notes, PDF pathology reports) and structured tables (e.g., lab results, basic subject information). Data updates frequently, especially during the screening phase, as subject indicators change dynamically. Document structures are complex, involving extensive medical terminology, abbreviations, and specific formats. Fields and units include RECIST assessment results, ECOG scores, various blood count indicators (e.g., PLT values in 10^9/L), liver and kidney function indicators (e.g., ALT values in U/L), and gene mutation types (e.g., EGFR T790M).
Deployment and Upgrade Constraints from Data Characteristics
The diversity and complexity of bispecific antibody clinical trial pre-screening data impose specific requirements on FastGPT deployment and upgrades. A high proportion of unstructured text demands robust text parsing capabilities and multi-modal embedding model support. High data update frequency necessitates rapid incremental updates and timely index maintenance for the knowledge base. Complex document structures and specialized terminology limit the recall accuracy of general RAG models, requiring targeted configuration of segmentation strategies and re-ranking models. For example, different clinical trial protocols have subtle variations in inclusion/exclusion criteria, requiring the knowledge base to differentiate and extract these details. Medical fields demand extremely high data accuracy; any pre-screening error can lead to severe consequences. Therefore, model parameter tuning and robust error handling mechanisms are critical.
Configuration Decisions
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual files like clinical trial protocols and pathology reports can be large; this ensures successful upload. |
maxContext | 3000 Tokens | Handles long descriptions and multi-layered logic in complex clinical documents, ensuring context completeness. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic coherence in medical text with processing efficiency per segment, preventing semantic fragmentation. |
Rerank result count (Re-rank Return Count) | Top 10 entries (top 10) | Improves recall precision in medical information retrieval, filtering for more relevant clinical evidence. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient parsing time when processing large PDF clinical documents. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Determine through test sets, considering the specific characteristics of clinical trial data and pre-screening accuracy requirements. |
Common Pitfalls
- Symptom: FastGPT cannot create new knowledge bases or applications after logging in. Reason: No user registration or initial administrator account configuration after local deployment.
- Symptom: Uploaded clinical trial protocol PDF files time out during parsing, or some content is missing. Reason: The file content is too large or the structure is too complex, and the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, causing parsing to be interrupted before completion. - Symptom: Some critical genetic testing indicators are not recognized or are incorrectly identified in pre-screening results. Reason: The knowledge base lacks a synonym table for specific gene mutation names or units, preventing the embedding model from correctly understanding them.
Verification Steps
- Upload a typical clinical trial data set containing various document types (PDF, TXT, CSV). Check if all files are successfully parsed and generate retrievable knowledge segments.
- For a clinical trial protocol with known inclusion/exclusion criteria, query the model to verify its ability to accurately identify and explain key screening conditions, such as subject age range or specific biomarker levels.
- Perform a simulated pre-screening using a test set with diverse subject data. Check if the system's extraction and judgment of different indicators (e.g.,
ECOGscore,platelet count) meet expectations, and verify if the number of results matches the expected count.
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.