Data Characteristics
Rare disease clinical trial pre-screening data comes from various global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), medical literature databases (e.g., PubMed, Medline), rare disease-specific databases (e.g., Orphanet, OMIM), and patient registries. Data update frequencies vary; clinical trial registration information often updates quarterly or monthly, while genomics and proteomics data may update a few times annually. Document structures are diverse, including structured trial protocols, unstructured medical reports, semi-structured genetic test reports, and patient medical records. Fields and units include common medical indicators (e.g., complete blood count, biochemical indicators, with units like mmol/L, g/dL) and numerous specific gene mutation sites, phenotype descriptions (typically text), and disease staging (often Roman numerals or specific codes). Data volume is generally large but highly dispersed, with many non-standardized descriptions.
Constraints on Deployment and Upgrades from These Characteristics
The multi-source and heterogeneous nature of rare disease data requires FastGPT deployments to have robust data ingestion and preprocessing capabilities, especially for unstructured text and semi-structured reports. Frequent data updates mean the knowledge base needs to support incremental updates and version management to ensure pre-screening results are based on the latest information. Complex document structures and specific fields and units demand advanced tokenization, entity recognition, and knowledge graph construction. Traditional tokenization strategies might not effectively identify rare disease-specific terminology and gene loci. Additionally, rare disease data often involves highly sensitive patient information, so the deployment environment must meet strict data security and privacy standards, such as data encryption and access control. In offline deployment scenarios, model updates and knowledge base synchronization require well-defined mechanisms to prevent data staleness or model failure due to network limitations.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Rare disease clinical trial documents often contain large images or high-resolution charts, resulting in larger file sizes. |
maxContext | 3000 characters | Rare disease medical records and genetic reports are often lengthy, requiring a larger context window for semantic understanding. |
Chunk size | 800 characters | Balances semantic completeness of long texts with retrieval efficiency, preventing loss of critical information during segmentation. |
Similarity threshold | 0.75 | Rare disease phenotype descriptions and gene mutations are complex; a higher threshold reduces false positives and improves recall precision. |
Rerank result count | 10 entries | Ensures the model can re-rank more relevant documents after initial retrieval, capturing subtle connections. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing and OCR processes can be time-consuming, preventing processing failures due to timeouts. |
Common Pitfalls
- Knowledge base query results do not meet expectations or exhibit "weak intelligence." This often happens when pre-trained models are not updated or replaced after offline deployment, preventing the model from understanding new rare disease terminology and gene naming conventions.
- Image loading failures or missing content during document parsing. This typically occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too low, causing large or complex medical images and charts to fail rendering and OCR within the allotted time. - After upgrading FastGPT, some historical data becomes unusable, appearing as empty fields or abnormal data formats. This might be due to older knowledge base documents lacking metadata fields, such as
tmbId, required by the new version in the MongoAppVersion table.
Verification Steps
- Upload a PDF document containing rare disease-specific gene mutation sites and complex phenotype descriptions. Check the knowledge base segment preview to confirm that critical information and specialized terms are correctly tokenized and extracted.
- Perform a clinical trial pre-screening query for a specific rare disease. Compare the query results with known trial information to evaluate the relevance and accuracy of recalled literature, ensuring appropriate recall count and similarity threshold configurations.
- Simulate an incremental knowledge base update by importing a dataset with newly discovered rare disease gene variations. Then, perform a query to confirm the system can identify and utilize the new information for pre-screening.
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.