Data Characteristics for this Category
Autoimmune disease clinical trial pre-screening data primarily originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), pharmaceutical company internal research databases, and medical literature (e.g., PubMed, Medline). Data updates frequently. ClinicalTrials.gov typically updates weekly. Medical literature publishes continuously. Document structures vary. These include structured trial protocol summaries, unstructured research report PDFs, patient recruitment criteria text, and patient cohort screening results in CSV format. Common fields include disease diagnosis (e.g., ICD-10 codes), biomarker levels (e.g., ANA titer, RF value, units typically IU/mL or U/mL), medication history, adverse event codes (e.g., MedDRA codes), and inclusion/exclusion criteria descriptions.
Constraints from these Characteristics on "Citation and Traceability"
The heterogeneous nature of autoimmune disease data sources requires the knowledge base to handle multiple document formats. It must accurately identify and extract key information. High-frequency updates mean the knowledge base needs incremental updates and version management. This ensures the timeliness of cited sources. Complex document structures, especially lengthy clinical research reports, demand advanced segmentation strategies. These strategies must balance information completeness with retrieval efficiency. The presence of numerical fields like biomarkers makes precise matching and citation of numerical ranges important during pre-screening. Specialized terminology and abbreviations in inclusion/exclusion criteria require high semantic understanding in text processing. This ensures citation accuracy and avoids screening errors due to ambiguous terms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Balances context completeness for lengthy clinical reports with retrieval granularity, preventing information fragmentation. |
Recall count (Recall Count) | Top 8 | Autoimmune disease inclusion/exclusion criteria often involve multiple dimensions. Increasing recall covers more relevant information. |
Similarity threshold (Similarity Threshold) | 0.78 | Addresses the precise matching requirements for medical terminology. This raises the threshold to reduce interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 5 | After increasing recall, reranking selects the most relevant citations, optimizing the final presentation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles potentially long parsing times for large PDF research reports, preventing parsing interruptions. |
maxContext | 3000 | Ensures the large language model receives sufficient context information to process complex inclusion/exclusion criteria logic. |
Three Common Mistakes
- Knowledge base uploads of CSV files display garbled content. This usually occurs when the file encoding format does not match the system's default encoding. For example, the original file is
GBKbut the system parses it asUTF-8. - Citations do not appear in the body of knowledge base answers. The answer lacks source links or directly quoted text. This may be because the model's return format is not configured to include the
sourcefield, or the rendering logic does not process thesourcefield. - Configuring
maxContexttoo large (e.g., exceeding3000) causes context not to be sent to the large language model or results in a response timeout. This may be due to exceeding the model's or gateway's request length limits, or increased network transmission latency.
How to Verify Correct Configuration
- Upload clinical trial data in various formats (PDF, CSV, TXT). Check if file parsing is normal and content is complete and not garbled.
- Ask questions related to typical patient screening. Check if the answer includes citation links pointing to original knowledge base documents. Verify that these links trace back to specific paragraphs.
- Validate representative inclusion/exclusion criteria queries. Check if the model's cited text accurately supports the screening decision. Pay particular attention to biomarker numerical ranges and disease diagnosis terminology citations.
- In the knowledge base management interface, review recently updated data sources. Confirm that the incremental update mechanism works as expected and new data is retrievable and citable.
The values provided are common starting points. They 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.