Data Characteristics
Autoimmune disease clinical trial pre-screening data comes from multiple sources. These include Electronic Health Records (EHR), laboratory test reports, imaging reports, genomic data, and Patient-Reported Outcomes (PROs). Data update frequencies vary. EHR data may update in real-time, while genomic data is relatively static. Document structures are diverse. EHRs contain unstructured physician notes, structured diagnostic codes (e.g., ICD-10), and medication records. Lab reports are typically structured tables with test items, results, and units. Imaging reports primarily consist of semi-structured text descriptions. Fields and units are specific. Lab indicators have clear values and units (e.g., ANA titer, CRP mg/L). Genomic data involves SNP loci and allele information. PROs appear as scale scores or free-text descriptions.
Constraints on Model Integration and Configuration
The high heterogeneity of autoimmune disease data creates challenges for model integration. Unstructured text, such as physician progress notes, requires robust Natural Language Processing capabilities for entity recognition and relationship extraction. This extracts key disease symptoms, signs, and medication information. Structured data requires models to accurately parse fields and handle missing or anomalous values. Integrating multi-source data necessitates effective data preprocessing workflows. These workflows standardize data from different formats and update frequencies. For example, the static nature of genomic data may require additional logic. This logic determines if genomic data needs reloading or re-associating when processing real-time patient information. Autoimmune disease diagnosis is complex and ambiguous. Models must handle uncertain information and avoid overgeneralization. For instance, a symptom might relate to multiple autoimmune diseases. The model needs to integrate multi-dimensional information for comprehensive judgment.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 32000 token | Ensures the model can accommodate complex patient histories and multiple clinical documents without truncating critical information. |
Chunk size | 500-800 characters | Adapts to lengthy descriptions in autoimmune medical records. Balances recall efficiency with the semantic completeness of segments. |
Similarity threshold | 0.75 | Autoimmune disease symptoms often have high similarity. This threshold helps filter out irrelevant fuzzy matches, improving recall precision. |
Rerank result count | Top 10 entries | Considers the need for comprehensive information coverage during the initial screening phase. Improves the accuracy of subsequent model judgments. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Supports the parsing time required for large imaging reports or complex genomic reports. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates the upload of files containing high-resolution images or large genomic sequence data. |
Common Pitfalls
- The "AI Model" option in the model workflow is empty. This typically occurs when the corresponding language model service is not correctly configured or enabled in the system's global settings.
- The model outputs its thought process but no main body text. This may happen if the model's
stop_sequencesparameter is set incorrectly, causing the model to stop prematurely after its internal monologue. - The message "Model stream response is empty, please check if the model stream output is normal" appears. This often indicates a model call timeout or instability in the external model service, preventing FastGPT from receiving a valid response.
Configuration Verification
- Upload representative autoimmune medical record documents. Observe file parsing progress and the final knowledge base segmentation. Ensure critical information is extracted correctly.
- Construct test questions based on typical autoimmune clinical trial inclusion and exclusion criteria. Check if the model recalls accurate and comprehensive relevant document snippets.
- Use queries containing specific laboratory indicators and genetic variation information. Verify if the model can correctly identify and associate them with corresponding values and explanations.
- Simulate real pre-screening scenarios. Input case descriptions with ambiguous symptoms. Observe the model's reasoning process and preliminary judgments. Evaluate their alignment with expected outcomes.
The values provided are common starting points. Measure them against your 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.