Data Characteristics
Rare disease clinical trial pre-screening data originates from diverse sources. These include medical literature databases (e.g., PubMed, Medline), clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), patient registries, genomics databases (e.g., ClinVar, OMIM), and electronic health records (EHR). Data update frequency varies by source; clinical trial registries typically update weekly or monthly, while medical literature databases update daily or weekly. Document structures are complex. They include unstructured clinical notes, semi-structured clinical trial protocols (PDFs), and structured genomic test reports and patient characteristic tables. Fields include disease names, genetic mutations, clinical phenotypes (using HPO terms), drug treatment history, imaging reports, and biomarkers. Units often involve drug dosages (mg, μg), time (days, weeks, months), and biological indicator concentrations (ng/mL, pmol/L).
Constraints from "Model Integration and Configuration"
The multi-source and heterogeneous nature of rare disease data requires models to handle various data formats. The large volume of unstructured text (e.g., medical records and literature) makes text embedding models and Large Language Models (LLMs) central. Frequent data updates, especially for clinical trial registration information, demand high real-time capabilities for the knowledge base. This necessitates efficient incremental update mechanisms. Complex document structures, particularly PDFs containing charts and medical images, make multimodal model integration essential for extracting visual information. Inconsistent fields and units across different databases, along with pervasive specialized medical terminology, require models with robust entity recognition, relationship extraction, and unit standardization capabilities to ensure accurate information extraction. These constraints collectively prioritize model generalization, update efficiency, and multimodal processing capabilities during model integration and configuration.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8192 token | Rare disease clinical literature and medical records are often long. A sufficiently long context window is needed to understand the full disease picture and complex medical history. |
embeddingModel | text-embedding-ada-002 or higher version | Addresses semantic understanding of medical terminology and complex biological concepts, ensuring accuracy of high-dimensional vectors. |
chunkSize | 800–1200 characters | Balances semantic integrity and recall efficiency. Avoids over-segmentation leading to context loss, or excessively large segments introducing irrelevant information. |
overlapSize | 100–200 characters | Ensures smooth transitions between segments, preserving the relevance of adjacent segments. Reduces the risk of critical information being cut off. |
imageRecognition | Enabled | Clinical data often includes imaging reports and pathology slides. Multimodal capabilities help understand the patient's condition comprehensively. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF documents (e.g., clinical trial protocols) can be time-consuming. The file parsing timeout needs to be extended. |
Common Pitfalls
- When uploading images for recognition in an AI chat node, the model's response does not provide image content. This happens because it is configured as a text-based AI. This typically results from not correctly integrating a multimodal-capable model or not enabling image recognition in the node configuration.
- A connection error occurs when connecting to an externally deployed VLLM Qwen3-Embedding-8B model. This may be due to network configuration issues (e.g., firewall, proxy settings) or a mismatch between FastGPT's model calling interface parameters and the VLLM service's actual requirements.
- Model tool calls are interrupted. This may stem from a tool execution timeout or the tool's response format not meeting the model's expectations, causing the model to stop processing.
Verification
- Upload a clinical trial protocol PDF containing text and images. Verify that its content is correctly parsed and that key disease information, genetic mutation descriptions, and trial design details are extracted.
- Ask questions about a rare disease patient's structured genomic test report. Check the model's understanding of gene loci, mutation types, and related clinical phenotypes. Verify its ability to match these with registered clinical trials.
- Configure a query set containing common rare disease terms and abbreviations. Test and evaluate the accuracy and relevance of the literature and clinical trial information recalled by the model under different queries. Adjust the
similarity thresholdbased on actual needs. - After integrating a multimodal model, upload an image containing a pathology slide or medical image. Ask relevant questions. Verify that the model can extract effective information from the image and provide a reasonable response.
The values given are common starting points and should be measured 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.