Data Characteristics in This Category
Intelligent triage in biopharmaceuticals, specifically for clinical trial pre-screening, primarily uses data from public clinical trial registries (e.g., ClinicalTrials.gov, Chinese Clinical Trial Registry) and de-identified patient electronic health records (EHR) from hospitals. Data updates frequently. Clinical trial information updates daily or weekly, and patient records generate in real-time. Document structures vary. Clinical trial protocols are often unstructured text in PDF or Word format, containing detailed inclusion/exclusion criteria, study drugs, and research centers. Patient medical record data is mostly structured or semi-structured, such as diagnostic reports, lab results, and medication records, typically in JSON, XML, or CSV formats. Fields and units are highly specialized. For example, "age" units can be "years," "months," or even "days." "Weight" units include "kg" and "lb." "Laboratory indicators" like "hemoglobin" often include specific reference ranges and units (g/L, mmol/L).
Constraints Imposed by These Characteristics on Deployment and Upgrade
The diversity and specialized nature of clinical trial pre-screening data impose specific requirements on FastGPT's deployment and upgrade process. Parsing unstructured clinical trial protocols requires robust text processing capabilities. Setting chunk_size and overlap_size is crucial for extracting complex inclusion/exclusion criteria while maintaining semantic completeness. Structured and semi-structured patient medical record data demands flexible data import and mapping mechanisms in the knowledge base to prevent information loss or misinterpretation due to field mismatches. Frequent data updates mean the system must support incremental updates and version management. For instance, after a new clinical trial is published or patient records are updated, the knowledge base should quickly synchronize and re-index. This directly impacts the reindex_interval parameter configuration. The biopharmaceutical domain requires extremely high data accuracy; any deviation in pre-screening results can affect patient safety and trial progress. Therefore, during upgrades, data migration integrity and consistency, along with model inference logic stability, must be ensured. In offline deployment scenarios, managing external dependency update packages and implementing internal image backup and recovery strategies are critical for system availability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial protocol PDFs are often large; ensure complete upload. |
chunk_size | 800–1200 characters | Balances semantic completeness of complex inclusion/exclusion criteria with recall efficiency. |
overlap_size | 100–200 characters | Ensures contextual connection between segments, preventing critical information from being split. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large PDFs or multi-page texts. |
reindex_interval | 24 hours | Accommodates daily update frequency for clinical trial information and patient records. |
Recall count | Top 10 entries | Improves pre-screening recall rate, covering more potentially matching trials. |
Three Common Mistakes
- After importing an Excel file into the knowledge base, content is incorrectly segmented, leading to inaccurate RAG results. This usually happens when
chunk_strategyis not correctly specified for multi-column structured data or pre-processing is not performed. - After a system upgrade, some query results are abnormal or login fails. This might be due to incorrect migration or update of database configurations or dependencies during the upgrade, preventing the service from starting normally or causing data access errors.
- Clinical trial pre-screening results do not meet expectations, failing to match obviously eligible patients or trials. This often indicates an inappropriate
Similarity threshold(similarity threshold) setting or outdated knowledge base data that does not include the latest information.
How to Verify Configuration
- Upload a clinical trial protocol PDF containing complex inclusion/exclusion criteria. Check if knowledge base segmentation is reasonable and if critical information is fully extracted.
- Import a de-identified patient medical record CSV file with various field types via API or the management interface. Verify that data is successfully stored and can be effectively retrieved.
- Simulate a system version upgrade. After the upgrade, execute a predefined set of test cases. Compare the consistency of intelligent triage results before and after the upgrade.
- Query clinical trials for a specific disease or drug. Verify that the recalled trial list meets expectations and that detailed information for matched patients is accurate.
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.