Data Characteristics
IVD diagnostic reagent clinical trial pre-screening data primarily comes from multi-center clinical trial reports, raw patient medical records (e.g., lab results, imaging reports), subject informed consent forms, ethics approvals, product manuals, and registration documents. Data update frequency typically aligns with clinical trial phase report submission cycles, potentially monthly, quarterly, or annually. Document structures vary, including PDF reports, Word protocol documents, Excel lab data, and structured data exported from LIS/PACS systems. Fields and units are highly specialized. For example, "serum creatinine" uses umol/L, and "tumor marker CA-125" uses U/mL. This involves extensive medical terminology, abbreviations, and proper nouns.
Constraints Imposed on Deployment and Upgrade by Data Characteristics
Data source diversity requires robust heterogeneous data ingestion capabilities during deployment, supporting various file format parsing. The longer update frequency means knowledge base construction needs to incorporate historical data version management, ensuring pre-screening logic relies on the latest, audited datasets. Document structure complexity demands high performance from content parsing modules. These modules must identify and extract key information from unstructured documents, such as trial inclusion/exclusion criteria and diagnostic thresholds. The specialized nature of medical fields and units requires accurate semantic understanding of terminology during knowledge vectorization and retrieval. This avoids misjudgments due to unit or abbreviation differences and may necessitate customized tokenizers and entity recognition models.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial reports or raw data files are often large. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word documents takes a long time. |
maxContext | 3000 Tokens | Ensures coverage of longer clinical trial protocols or medical record summaries. |
Chunk size | 800–1200 characters | Balances clinical trial item completeness and vector retrieval efficiency. |
Recall count | Top 10 entries | Increases recall rate of relevant information, covering more potential screening criteria. |
Similarity threshold | 0.8 | Ensures high relevance between retrieval results and IVD diagnostic reagent pre-screening conditions. |
Three Common Mistakes
- After uploading an attachment, the system does not summarize and analyze it. There are no errors during communication, but the attachment content is not recognized by the knowledge base. This usually occurs when the container environment cannot access external dependencies or internal services, causing the file parsing service to fail to start or complete.
- An
AxiosError 404occurs when calling a plugin. This might be due to FastGPT container internal network configuration issues, preventing correct resolution or access to external API endpoints, or the plugin service not being deployed correctly. - Diagnostic thresholds or unit judgments are incorrect in the pre-screening results. This often happens when the knowledge base construction fails to correctly handle medical terminology synonyms or abbreviations, or when unit sensitivity is insufficient during vectorization.
How to Verify Correct Configuration
- Upload an IVD diagnostic reagent clinical trial protocol PDF containing complex tables and medical terminology. Test through dialogue whether inclusion/exclusion criteria can be accurately extracted.
- Simulate a subject case, including specific lab test results (with units). Verify if the system can provide correct pre-screening judgments based on diagnostic thresholds in the knowledge base.
- Check FastGPT container network configuration. Ensure all internal services and external API endpoints are accessible. Verify connectivity using ping or curl commands.
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.