Data Characteristics for Solid Tumor Clinical Trial Pre-screening
Data for solid tumor clinical trial pre-screening originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), medical literature databases (e.g., PubMed, Embase), oncology hospital Electronic Health Record (EHR) systems, and genetic sequencing reports. Update frequencies vary: registries may update weekly, literature databases continuously publish new articles, and EHR data changes in real-time. Document structures are diverse, including structured trial protocol summaries, unstructured investigator brochures (IBs), text descriptions of inclusion/exclusion criteria, and semi-structured patient diagnostic reports and genetic test results. Field and unit specificities include disease staging (e.g., TNM staging), tumor marker concentrations (e.g., ng/mL), gene mutation types (e.g., EGFR L858R), and treatment history (e.g., number of prior chemotherapy cycles). These require precise identification and parsing.
Deployment and Upgrade Constraints from Data Characteristics
The wide range and heterogeneity of solid tumor data sources require FastGPT to integrate multiple data source connectors during deployment and possess robust unstructured text parsing capabilities. Varying update frequencies mean the system must support flexible data synchronization strategies. For example, periodic full or incremental synchronization for registries, continuous monitoring for new literature, and potentially real-time API interfaces for internal EHRs. Diverse document structures place higher demands on the data preprocessing module. For instance, chunking strategies need optimization for different document types to ensure information completeness and contextual continuity. The specificity of fields and units requires FastGPT's Retrieval Augmented Generation (RAG) module to understand and precisely match medical terminology, avoiding misjudgments due to unit or expression differences. This may also necessitate customized post-processing logic to extract key numerical values and classification information. The deployment environment needs sufficient computing resources to handle the cleaning, embedding, and index building of large volumes of heterogeneous data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial protocols and investigator brochures are often large; this ensures single-file upload processing capability. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing and OCR recognition can be time-consuming; this prevents parsing failures due to timeouts. |
Chunk size | 800 characters | Solid tumor inclusion/exclusion criteria descriptions are detailed; this ensures each segment contains sufficient contextual information. |
Recall count | Top 8 entries | Increases the number of relevant inclusion/exclusion criteria and patient characteristics retrieved from the vector database. |
Similarity threshold | 0.75 | Precisely matches medical terms and clinical features, reducing interference from irrelevant information. |
maxContext | 32000 token | Ensures sufficient context when generating pre-screening results to comprehensively determine patient eligibility. |
Common Pitfalls
- Invalid file content extraction: Uploaded PDF or image format medical report content is not correctly recognized, leading to empty retrieval results. This occurs due to missing necessary OCR services or improperly installed dependency libraries in the deployment environment, or incompatible file encoding/format.
- Inaccurate or missing search results: After a user enters patient characteristics, the system fails to retrieve relevant clinical trial information, or the retrieved trials have a low match accuracy. This occurs because of insufficient standardization of medical terminology during the data preprocessing stage, leading to low-quality vector embeddings, or chunking strategies that fail to effectively preserve key information.
- Login-free window inaccessible: After deployment, when accessing via a login-free link from another computer, the page fails to load or displays a connection error. This occurs because the FastGPT service is not correctly configured with an external access address (
APP_URL) or firewall policies block external requests, preventing access from a local area network or the public internet.
Verification Steps
- Upload a PDF version of a clinical trial protocol containing complex medical terminology and charts. Check if the file content, especially key information in the inclusion/exclusion criteria section, is extracted completely and accurately.
- For different tumor types (e.g., lung cancer, breast cancer) and treatment stages (e.g., first-line, second-line), input detailed simulated patient condition descriptions. Observe if the retrieved clinical trial list matches expectations and if key fields in the retrieved entries (e.g., indications, inclusion/exclusion criteria) are accurate.
- On a device other than the deployment server, access FastGPT via the configured login-free link and attempt a pre-screening query. Confirm that the page loads correctly and functionality is available.
- Check FastGPT's log output to confirm that data synchronization tasks are executing as planned, without a large number of parsing failures or connection timeout errors.
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.