Deployment and Upgrade for Market Access Clinical Trial Pre-screening

Data Characteristics for This Category

Market access clinical trial pre-screening data originates from global clinical trial registries (e.g., ClinicalTrials.gov, EU Clinical Trials Register), regulatory approval documents, medical literature databases (e.g., PubMed, Embase), and commercial databases. Update frequencies vary. Clinical trial registration information typically updates daily or weekly. Approval documents may update monthly. Medical literature publishes continuously. Document structures are complex, containing both structured data (e.g., trial ID, indications, drug names, sponsors, clinical trial phases, primary endpoints, secondary endpoints) and extensive unstructured text (e.g., trial protocol summaries, inclusion/exclusion criteria descriptions, adverse event reports). Fields and units include dosage (mg/kg), treatment cycles (weeks/months), patient numbers, biomarker concentrations (ng/mL), and often contain medical terminology and abbreviations.

Constraints on Deployment and Upgrade Due to These Characteristics

The complexity of market access clinical trial pre-screening data imposes specific requirements on FastGPT deployment and upgrades. Diverse data sources and varying update frequencies necessitate flexible data ingestion pipelines. These pipelines must support scheduled fetching and incremental updates of heterogeneous data, avoiding full re-indexing. Extensive unstructured text and medical terminology in documents require enhanced medical vocabulary recognition and standardization during text processing. This ensures embedding models accurately understand professional contexts. For example, precise parsing of dosage and cycle information within inclusion/exclusion criteria is crucial for subsequent conditional filtering. High-frequency incremental data updates demand high real-time performance and indexing capabilities from the vector database. This requires optimizing embedding generation and index reconstruction strategies. Furthermore, given the sensitive drug development information involved, deployment environment security and data isolation mechanisms are important considerations.

Configuration Settings

Three Common Mistakes

• A code execution component in a workflow errors with "ModuleNotFoundError: No module named 'mineru'". This typically indicates a missing third-party Python library in the deployment environment. Ensure mineru or other specific data processing libraries are correctly installed.
• After uploading a large clinical trial report PDF, the system remains unresponsive for an extended period or displays "file parsing timeout". This suggests the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, not allowing sufficient time for file parsing.
• When searching for clinical trial information on a specific drug, the number of results is significantly low, or expected documents are not recalled. Possible reasons include a Chunk size that is too short, causing critical information to be split, or a Similarity threshold that is set too high.

How to Confirm Correct Configuration

• Upload and parse several typical large clinical trial protocol PDF files. Check if the file content is fully imported without timeout errors.
• Execute a series of queries containing medical terminology and abbreviations. Verify the accuracy and relevance of recall results. Adjust Similarity threshold based on recall count and similarity scores.
• Through FastGPT's data management interface, inspect imported clinical trial data. Confirm that structured fields (e.g., 试验阶段, indications) and unstructured text (e.g., 入排标准) are correctly extracted and searchable.

Note: The values provided are common starting points. Measure against your own samples for optimal configuration.

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.