Data Characteristics
Infectious disease clinical trial pre-screening data originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), biomedical literature databases (e.g., PubMed, Embase), and data reports from disease control and prevention agencies. This data updates frequently; clinical trial registries typically update weekly, and literature databases may update daily. Document structures vary. Clinical trial registration information primarily consists of structured fields, including study status, inclusion/exclusion criteria, interventions, and primary outcome measures. Literature data primarily consists of unstructured text, including research background, methods, results, and discussion. Regarding fields and units, disease diagnosis often involves ICD codes, microbial identification involves sequence or phenotypic data, and efficacy assessment may include viral load (copies/mL), bacterial colony count (CFU/mL), or clinical symptom scores (e.g., NEWS score).
Constraints on Deployment and Upgrade from Data Characteristics
The high update frequency of infectious disease data requires FastGPT's data synchronization mechanism to support efficient incremental updates, ensuring the timeliness of pre-screening models. The heterogeneous nature of data sources (structured and unstructured data coexist) means that deployment requires configuring various data connectors and parsers to accommodate different data ingestion formats. Specifically, unstructured text in literature, with its complex medical terminology and pathogen variation information, affects the accuracy of RAG retrieval, requiring more refined segmentation strategies and embedding models. The specificity of fields and units, such as viral load or bacterial counts, requires the knowledge base to correctly identify and process this numerical information during construction, preventing unit confusion or numerical misjudgment during pre-screening matching. During deployment, synchronous upgrades of model versions and knowledge base versions are crucial to ensure new data is effectively understood and utilized by the latest models.
Configuration Guidelines
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 4000 characters | Infectious disease literature often contains extensive medical details, requiring a longer context window to understand complete disease courses and treatment plans. |
Chunk size (Segment Length) | 800 characters | Balances semantic completeness of long texts with retrieval efficiency, avoiding truncation of critical medical descriptions or inclusion/exclusion criteria. |
Similarity threshold (Similarity Threshold) | 0.75 | Clinical manifestations and treatment plans for infectious diseases often have high similarity, requiring a higher threshold to reduce false positives. |
Embedding Model | text-embedding-ada-002 | This model performs well in semantic understanding and similarity calculation for biomedical domain texts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF clinical trial reports or research protocols requires a longer file parsing timeout. |
Knowledge Base Auto Sync Frequency | Daily at 2:00 AM | Ensures clinical trial registries and the latest literature data are updated in the knowledge base promptly, maintaining the timeliness of pre-screening information. |
Common Mistakes
- Symptom: Irrelevant clinical trials or literature frequently appear in pre-screening results. Cause: The knowledge base construction did not adequately utilize disease-specific terminology for keyword extraction or semantic weighting.
- Symptom: A FastGPT instance deployed locally with Docker experiences database connection errors after several hours of operation. Cause: The
PG_MAX_CONNECTIONSparameter is set too low, failing to handle concurrent data processing and query requests, leading to database connection pool exhaustion. - Symptom: After upgrading the FastGPT version, some model channels fail to invoke correctly, returning 4xx errors. Cause: The new version adjusted model API interfaces or authentication methods, but
OPENAI_API_KEYorCUSTOM_MODEL_URLwere not updated accordingly.
Verification Steps
- Upload a clinical trial protocol document containing typical infectious disease inclusion/exclusion criteria. Check the accuracy of knowledge base segmentation and keyword extraction, especially for pathogen names, diagnostic methods, and efficacy indicators.
- Perform a pre-screening query using a patient characteristic description for a specific infectious disease (e.g., a certain influenza virus strain infection or drug-resistant bacterial infection). Verify that the returned list of clinical trials highly matches the disease type, stage, and treatment plan, and check that inclusion/exclusion criteria are correctly identified.
- Simulate a high-concurrency query scenario. Observe system metrics such as
CPU_USAGEandMEMORY_USAGEto ensure that system response times remain within an acceptable range as data volume and query load increase.
The values given 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.