Data Characteristics in This Category
Clinical trial pre-screening decision support data primarily comes from clinical study protocols, patient electronic health records (EHR/EMR), medical imaging reports, and various medical guidelines and literature. This data typically exists as unstructured text, semi-structured tables, and structured databases. Regarding update frequency, clinical study protocols are relatively stable during the trial design phase but may update due to revisions during the trial. Patient EHR data is generated in real-time, leading to high update frequency. Medical literature and guidelines are regularly published or revised. Document structures vary; for example, study protocols include detailed sections on inclusion/exclusion criteria and trial procedures, while EHRs have standard fields like chief complaint, history of present illness, past medical history, and examination results. Fields and units are highly specialized in medicine, such as laboratory indicators involving different units like mmol/L and ng/mL, disease diagnoses using ICD-10 codes, and drug dosages in mg or g.
Constraints on Deployment and Upgrade from These Characteristics
Data source diversity requires the system to have robust heterogeneous data access capabilities during deployment, supporting various file formats. The high update frequency of patient EHR data means the knowledge base needs to support incremental updates and real-time indexing, avoiding service interruptions from full rebuilds. Regular updates to medical literature and guidelines necessitate a smooth upgrade process to replace older knowledge base versions while retaining historical versions for traceability. The presence of unstructured text and specialized fields demands high accuracy in text preprocessing and entity recognition; deployment must ensure accurate loading of relevant models and dictionaries. The existence of multiple units and coding systems requires the RAG retrieval model to understand and handle unit conversions and code mapping, preventing misjudgments due to inconsistent units. The deployment environment needs sufficient computing and storage resources to handle large volumes of medical text processing and indexing, especially when supporting multi-replica deployments, where resource planning is critical.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical study protocols and medical records may contain many images and charts, leading to large file sizes. |
maxContext | 4000 | Clinical trial inclusion/exclusion criteria and patient medical history descriptions are often long, requiring more context. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents can be time-consuming; this prevents timeout errors. |
Chunk size | 800–1200 characters | Ensures each segment contains a complete medical concept or logical unit, improving retrieval accuracy. |
Recall count | Top 10 entries | Clinical decision support requires more comprehensive information to aid judgment. |
Similarity threshold | Calibrate based on actual measurements, starting around 0.75 | Ensures recalled medical text is highly relevant to the query, filtering out noise. |
Common Pitfalls
- Slow knowledge base indexing, or even prolonged unresponsiveness. This occurs when medical text is not effectively preprocessed, for example, by removing irrelevant symbols or standardizing terminology, leading to inefficient vectorization.
- After deploying FastGPT, some images fail to pull or download exceptionally slowly. This is typically due to network environment restrictions or improper mirror source configuration, preventing access to official or default Docker image repositories.
- RAG retrieval results show unit confusion or coding mismatches. This happens when the units and coding systems of specialized medical fields are not correctly identified and processed during data ingestion, leading to the retrieval model's inability to accurately understand.
Verification Steps
- Upload a clinical study protocol PDF file containing complex medical terminology and multi-unit descriptions. Verify that the file parses successfully and that key information can be searched normally within the knowledge base.
- Use the
docker logs <ContainerID>command to check the log output of FastGPT core services, vector database, and model services. Confirm there are no obvious startup errors or connection anomalies. - In the FastGPT application, input a query containing a specific disease, drug, and laboratory indicator. Check if the returned results include relevant knowledge base content and if key information (e.g., dosage, diagnostic codes) matches the original document.
- Simulate high-concurrency knowledge base query scenarios. Observe if the system response time is within an acceptable range. Use system monitoring tools to check if CPU, memory, and disk I/O usage are normal.
The values provided are common starting points and 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.